Reasoning AI Models: An overview

TL;DR

As part of my role at Microsoft’s AI Foundry Applied AI engineering team in CoreAI, I have participated in numerous detailed discussions about the evolving landscape of AI models. In conversations with many customers, from CxOs to engineers, one recurring topic is the rise of reasoning AI models. These models are designed to perform complex tasks by explicitly breaking down problems into logical steps, rather than just generating text in a single pass like traditional large language models (LLMs). This shift toward reasoning-centric AI marks a major evolution in how we develop and deploy AI systems—and it’s a key factor behind the rise of Agents and Agentic AI.

At the same time, there is a lot of confusion about what these reasoning models are, how they differ from traditional LLMs, and how to effectively adapt and evaluate them. In this post, I aim to clarify these concepts by providing a technical deep dive into reasoning AI models, their training and adaptation processes, and the challenges involved in fine-tuning them for specific tasks. We will also explore how to evaluate these models effectively, considering their unique characteristics.

This post is intended to help one gain a deeper understanding of reasoning models and their implications; I cover these areas:

• What are reasoning AI models? A technical overview of their architecture and training paradigms.
• How do they differ from traditional LLMs? Key distinctions in capabilities and performance
• How to adapt and fine-tune reasoning models? Best practices and common pitfalls
• What are the challenges in customizing them? Technical and organizational hurdles
• How to evaluate reasoning models? Metrics and strategies for assessing their performance

1. Introduction

Recent AI models have begun to combine language generation with explicit reasoning, enabling more reliable solutions to complex problems. Traditional LLMs like GPT-4o complete a generation in one go, without showing their work. Reasoning models, on the other hand, produce a sequence of intermediate steps (a “reasoning trace”) before the final generation. For example, Microsoft’s Phi-4-Reasoning (14B parameters) will explicitly work through a math problem step-by-step, whereas a regular LLM might confidently state an answer with no explanation. This fundamental difference – predictive text generation vs. chained logical reasoning – makes reasoning LLMs significantly better at multi-step tasks, such as math word problems, code debugging, or complex decision queries.

Note: The AI model landscape is also shifting rapidly, with a newer trend of transitioning from separate “base” vs. “reasoning” models (e.g., o1/o3) to unified systems with internal routing (e.g., GPT-5). GPT 5 runs a system that routes between fast and deliberate paths and exposes developer controls to tune thinking time. In production, the system automatically switches modes; developers can cap or elevate effort as needed. This operationalizes dynamic compute allocation, reducing the need for prompt engineering, specifically when wanting to induce reasoning.

The shift toward unified systems like GPT-5 can be understood as operationalizing the compute-optimal scaling insights from research. Rather than requiring users to choose between reasoning modes manually, these systems implement automatic difficulty assessment and adaptive compute allocation - essentially embedding the “compute-optimal” strategy within the model architecture itself.

1.1 What are reasoning models?

Reasoning models are LLMs architected to solve problems via a multi-step chain-of-thought (CoT) approach. Instead of just predicting the next token, they simulate an internal “scratchpad” of logic. For instance, OpenAI’s latest models (o1 and o3) reportedly allocate extra computation at inference-time and use reinforcement learning (RL) fine-tuning to boost multi-step reasoning. DeepSeek’s R1 (671B-parameter Mixture-of-Experts model ) was explicitly trained with multi-stage reinforcement learning to encourage step-by-step thinking.

During training, such models may be given examples formatted like: *Question → (Begin reasoning) → ... reasoning steps ... → (Final answer)*, or prompted with cues like “Let’s think step by step.” This teaches the model to articulate intermediate steps instead of jumping straight to an answer. In essence, a reasoning LLM learns to internalize a logical process – it doesn’t just know facts or language, it learns how to solve problems by breaking them down.

Crucially, these reasoning models often use special tokens to separate the “thinking” from the final answer. Many use a convention such as <think> ... </think> tags to enclose the chain of thought. For example, DeepSeek-R1-Distill (a distilled 8B version of R1) will output a hidden “thinking” transcript between these tags, followed by a concise answer that summarizes the reasoning. The chain-of-thought (CoT) might include equations, logic, or code, which the model generates as if working on scratch paper, and then the answer is given separately. This behavior is usually built into the model through fine-tuning – if you prompt such a model normally, it will, by default, produce a step-by-step solution trace and then provide the answer.

Some recent systems even let developers toggle the visibility of this trace: e.g., Qwen-3 allows a “reasoning mode” where the chain of thought is shown or hidden as needed. The key point is that reasoning models carry out more computation in the open, and they may consume more tokens. It is quite common for them to use hundreds or thousands of tokens for a complex solution, whereas a regular LLM might try to produce an answer in, say, a single paragraph.

1.2 Cognitive Architecture Parallels - Type 1 and Type 2 Thinking

The reasoning model paradigm directly parallels the Type 1/Type 2 thinking framework popularized by Daniel Kahneman . Some of the recent work demonstrating how LLMs can be aligned to either System 1 (intuitive and fast) or System 2 (analytical and deliberate) thinking patterns.

Type 1 thinking in AI systems corresponds to the pattern-matching and intuitive responses characteristic of traditional LLMs - fast, automatic responses based on learned patterns. Type 2 thinking represents the deliberate, step-by-step reasoning that reasoning models are designed to emulate. Research shows that System 2-aligned models excel in arithmetic and symbolic reasoning, while System 1-aligned models perform better in commonsense tasks.

Cognitive Flexibility and Performance Trade-offs

Unlike human cognition, which fluidly adapts between System 1 and System 2 thinking based on context, current LLMs lack this dynamic flexibility. This rigidity can lead to brittle performance when tasks deviate from trained patterns. However, reasoning models attempt to address this limitation by incorporating explicit System 2-style processing.

The research demonstrates an “accuracy-efficiency trade-off” where System 2-aligned models show greater uncertainty and more systematic processing, while System 1-aligned models provide more definitive but potentially less reliable answers. This suggests that optimal AI systems may need to switch between reasoning modes dynamically based on task complexity.

From an architectural perspective, reasoning LLMs are still transformer-based neural networks at their core. They don’t necessarily have new algorithmic components beyond the training tweaks, though some research explores adding tools or memory. It’s the training paradigm that sets them apart.

For example, where a classic 4o/4.1 style LLM is trained purely on next word prediction and maybe a bit of instruction tuning, a reasoning model like R1 or Phi 4 is trained in an extensive multi stage training pipeline (e.g. supervised fine tuning on curated CoT examples), then specialized reinforcement learning (using rewards for getting answers right and for producing a consistent chain of thought), and so on. OpenAI’s o1/o3 models are rumored to undergo similar multi-stage refinement, combining RL with the ability to allocate more thinking steps at runtime.

1.3 Chain-of-Thought: Built-in vs Prompted

Start by understanding what a chain of thought (CoT) is. CoT is the model’s “scratchpad”: a sequence of intermediate reasoning steps it writes out before giving the final answer. Many models fence this trace with special tokens (e.g., <think> ... </think>); there are configurations that can show or hide these. The advantage this gives us is better results on multi-step tasks (such as math, code, and planning) by decomposing problems. On the other hand, the trade-offs include more tokens → more cost/latency; and traces can be verbose or unfaithful if not evaluated. As a result, CoT is best used for complex queries, and where possible, it would be wise to consider either skipping or limiting these for simple lookups. See “Evaluation” for token-normalized accuracy and faithfulness checks.

CoT prompting emerged as a technique to enhance traditional LLMs by explicitly requesting step-by-step reasoning through prompts such as “Describe your reasoning in steps” or “Explain your answer step by step.” This approach leverages LLMs’ ability to “think out loud” in natural language, with effectiveness scaling with model size as an emergent ability.

Figure 2 shows an LLM decomposing a complex math word problem into sequential subquestions, solving each step before arriving at the final answer. (Credit: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models)

Reasoning models fundamentally differ in that they integrate CoT processing directly into their architecture and training process. Rather than requiring explicit prompting, these models automatically engage in step-by-step reasoning for complex tasks. Research indicates that “Chain-of-Thought built into the core architecture and training process” represents a more robust approach than external prompting.

However, CoT prompting is not universally effective across all models and tasks. Recent research on strategic reasoning has shown that CoT prompting is not universally effective, as it increases strategic reasoning only for models at certain levels, while providing limited gains elsewhere. This suggests that integrating reasoning capabilities requires careful architectural considerations beyond simple prompting strategies.

The effectiveness of CoT in reasoning models also varies by task complexity and domain. Models trained with reinforcement learning on reasoning tasks show more consistent application of multi-step reasoning compared to models relying solely on prompted CoT.

1.4 Test-Time vs Train-Time Compute

A critical innovation in reasoning models is the emphasis on test-time compute scaling. While their training parameters limit traditional LLMs, reasoning models can allocate variable computational resources during inference. OpenAI reports that the performance of o1 improves with more RL (train-time compute) and with more time spent thinking (test-time compute) ( overview ). This creates new scaling paradigms, where models can allocate more computational resources to harder problems during inference.

This inference-time compute scaling (using more tokens/steps) is a defining trait – it enables even smaller models to solve hard problems by iterating through reasoning. As Microsoft’s team describes, “Phi 4 Reasoning generates detailed reasoning chains that effectively leverage additional inference time compute,” allowing a 14B model to compete with far larger ones.

Because this extra “thinking” consumes tokens and compute, it helps to formalize the tradeoff to understand the concept better.

Test-time compute is best understood as a way to reshape the model’s output distribution at inference by searching over alternative reasoning paths and then selecting among them. It reliably lifts accuracy—especially on problems with verifiable answers—yet it is not interchangeable with pretraining compute.

Recent evidence shows that test-time compute helps most when the base model is already capable and the gap to the target difficulty is modest; on the hardest items, pretraining capacity still dominates - as outlined in Figure 3 below.

A practical rule is to treat thinking tokens as a budgeted resource: use them to explore and score candidate chains (branching) and reserve a small budget for targeted revision when a verifier flags issues. In cost terms, this gives you predictable returns without pretending that more inference tokens can fully substitute for more capable pretraining.

Test-Time Compute vs Model Size Trade-offs

A groundbreaking finding from recent research is that on problems where smaller models achieve non-trivial success rates, test-time compute can be used to outperform models 14× larger in FLOP-matched evaluations. This suggests a fundamental shift in how we think about compute allocation:

• Easy to medium problems: Test-time compute is often more effective than pretraining larger models
• Very hard problems: Pretraining capacity still dominates, with limited benefits from test-time scaling
• Practical implication: Rather than focusing purely on scaling pretraining, it may be more efficient to train smaller models and apply test-time compute strategically

Efficiency trade-off: How much “thinking” is enough?

OpenAI’s o1 explicitly reports: performance improves with more RL (train-time compute) and with more time spent thinking (test-time compute). Microsoft’s Phi-4 Reasoning (14B) shows similar patterns: small models, when allowed longer structured chains, outperform their weight in math/science. To examine the implications, consider a back-of-the-envelope cost model. If $L_r$ is “reasoning” length and $L_a$ is final answer length, a crude attention-heavy cost proxy is

$$ \text{Compute} ;\propto; H,(L_a + L_r)^2,d, $$

with hidden size $H$ and depth $d$. You can wrap this into an objective that matches your reality:

$$ \min_{L_r}; C(L_r) = \alpha,H,(L_a+L_r)^2,d ;+; \beta,\text{latency}(L_r) ;-; \gamma,\text{Acc}(L_r), $$

where $\alpha,\beta,\gamma>0$ are your infra cost, SLA pain, and value of accuracy. You won’t solve this analytically in prod—you’ll sweep the thinking budget and pick a knee point.

What is really interesting is that accuracy is typically concave in $(L_r)$; i.e, the first ~100–300 “thinking” tokens help a lot; beyond that, diminishing returns.

Quick intuition: if $L_a$ is small and $L_r$ doubles, the attention term grows by about $4\times$, while accuracy typically improves far less—hence token budgets and early stop heuristics. We’ll revisit this idea in Evaluation via token-normalized accuracy.

This trade-off also motivates practical features, such as token budgets, early-stop heuristics, and “fast vs. deliberative” paths (e.g., Qwen-3’s reasoning mode). With that lens, let’s look at what differs under the hood.

• More deliberate thinking often helps—up to a point - You can trade thinking tokens for accuracy on complex items.
• Returns diminish - the first ~100–300 “reasoning” tokens carry a lot of the lift; beyond that, you’re paying for a long tail.

Rule of thumb. Treat “thinking tokens” as a first-class budget; log it, control it, and optimize it like you optimize memory or p95 latency. Some model providers like Qwen 3 and NVIDIA’s NIM expose this thinking budget directly.

In short, reasoning LLMs are LLMs with a logic upgrade – through additional training, they learn to use reasoning strategies that standard models lack.

1.5 Effectiveness and Limitations of Reasoning LLMs

Recent benchmarks indicate that CoT reasoning yields significantly improved performance on complex tasks (see Figure 4 below). For example, Microsoft’s Phi-4-Reasoning models, with only 14B parameters, match or surpass much larger models in math and science benchmarks—sometimes even outperforming a model 5x times their size (surpassing OpenAI’s o1-mini, and R1’s 70B distilled version on many math and science benchmarks). This success is attributed to reasoning-focused training and reinforcement learning, proving that with strategic training, smaller models can excel at challenging tasks without needing massive scale. This demonstrates a general trend: with the right training, a model doesn’t have to be huge to solve complex tasks – it just needs to learn how to use its capacity more algorithmically.

Another data point is the DeepSeek R1 family. The original R1 (671B, MoE) was a “reasoning-maximal” model (see Figure 1), pushed to an extreme scale and trained with novel RL algorithms (such as GRPO, a group-based self-improvement method) to excel at long-horizon problems. Distilled smaller versions of R1 (70B, 8B, etc.) inherited some of these skills through knowledge distillation. These distilled reasoning models, even at 8B, achieved math and puzzle-solving scores significantly higher than those of similarly sized generic LLMs. Open-source efforts like Bespoke-Stratos-7B and OpenThinker-7B followed suit, demonstrating that a properly fine-tuned 7B model with CoT can outperform naive 7Bs by significant margins on benchmarks. In research from late 2024, Qwen-3 (an advanced open model by Alibaba) was released in both “thinking mode” and “no thinking” mode. Running Qwen-3 in its CoT mode, it actually outperformed DeepSeek-R1 on a majority of evaluated tasks despite activating only a subset of its parameters at each token (it’s a mixture-of-experts model, effectively).

What is interesting is that when Qwen-3 was toggled off (i.e., no CoT visible), it still beat a GPT-4-sized baseline on many benchmarks, implying that integrating reasoning steps did not harm its base competency – it only added the ability to dig deeper when needed. All these examples underscore that reasoning LLMs hold a significant edge on tasks that aren’t straightforward single-step predictions. Whenever an answer requires multiple pieces of information or intermediate calculations, a traditional LLM often fails or guesses incorrectly, whereas a reasoning LLM can navigate the steps systematically (much like a human showing their work). The gap is so notable that analysts have called reasoning LLMs “a critical evolution” in AI capability, and enterprise users are exploring them for decision-making support where correctness takes precedence over brevity.

Mathematical and Logical Reasoning

Reasoning models demonstrate substantial improvements over traditional LLMs in mathematical and logical reasoning tasks. OpenAI’s o1 achieves remarkable performance, ranking in the 89th percentile on competitive programming questions (Codeforces) and placing among the top 500 students in the US in a qualifier for the USA Math Olympiad (AIME).

Codeforces: Codeforces is a major competitive programming platform and community. It hosts frequent online contests (“Rounds”) where participants solve algorithmic problems within time limits, and it maintains an Elo-like rating system and color-coded titles (ranging from Newbie to Legendary Grandmaster).

Comprehensive evaluations (see Figure 5) show that o1-preview demonstrates 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions and an 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. These results indicate performance that often meets or exceeds that of human experts in structured reasoning domains.

Domain-Specific Applications

Beyond mathematics, reasoning models show strong performance across diverse specialized domains. Evaluations indicate remarkable proficiency in anthropology and geology, demonstrating a deep understanding and sound reasoning in these specialized fields, as well as strong capabilities in quantitative investing, complemented by comprehensive financial knowledge. The models also demonstrate superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models.

Recent research with ReasonFlux-32B has demonstrated that smaller, specialized reasoning models can outperform larger, general models. On the MATH benchmark, ReasonFlux-32B achieves an accuracy of 91.2% and surpasses o1-preview by 6.7% while being trained with only 8 GPUs.

ReasonFlux - ReasonFlux is a template-driven, hierarchical RL approach to reasoning LLMs; instead of lengthening raw CoT, it plans over a library of thought templates and scales those at inference time, yielding strong math results in a 32B-parameter model.

However, this does not mean reasoning models dominate on every task. For very simple or single-step queries (e.g., straightforward fact lookups or classifications), a regular LLM might perform just as well and with less latency - it is using fewer tokens and does not have to generate a long explanation; more tokens mean more computation and slower responses. That said, many reasoning LLMs are designed to be flexible – they can shorten or skip the reasoning when it’s not needed. Some deployments use a “fast path” versus a “deliberative path” approach: run the model in normal mode for easy questions and only invoke full reasoning mode for complex ones. This dynamic compute allocation is a research area in itself (how to predict when to make a model think longer).

The token-budget mechanism in Qwen-3 is one example: it allows users to cap how many reasoning tokens the model can use, forcing it to decide what’s most important. Accuracy does improve with more tokens (e.g., from ~70% at 2K tokens to ~85% at 16K on a math test), but after a point, it is a matter of diminishing returns. The existence of such features highlights that reasoning LLMs introduce a new dimension – a time/accuracy trade-off. Traditional LLM evaluation is usual just one-dimensional – measuring accuracy or quality for a given fixed model output length. On the other hand, reasoning LLMs let us trade generation length for correctness. (Note: the Evaluation section will cover more details on how to measure).

1.6 Branching & Editing at Test Time (how to “spend” thinking compute)

Test-time compute isn’t just “more tokens”; it’s a way to reshape the model’s output distribution by searching for, and then selecting, better reasoning paths during decoding. In practice, this plays out along two complementary axes. The first is branching: generate multiple candidate chains and prefer the one that scores best under a process- or outcome-aware judge. The second is editing: let the model (and its tools) reflect on an initial attempt and revise it once or twice. Both strategies are ways of allocating limited thinking budget where it matters most.

On the branching side, simple best-of-N sampling remains a solid baseline, while beam or tree-style search makes exploration adaptive by spending more decoding on promising partial thoughts. Process-aware scoring—via a process reward model (PRM) or per-step self-evaluation—helps prune low-quality branches early; when ground truth isn’t available, self-consistency (majority voting across diverse chains) is a practical fallback. Two small but useful tricks from recent work are to branch early—keeping only the top few first-token continuations before decoding greedily—and to anneal temperature across tokens to reduce accumulated randomness as chains grow. Together, these make parallel exploration both cheaper and more reliable.

Editing tackles a different failure mode: an answer that looks plausible but hides a local mistake. Here, short reflect-revise loops work best when anchored to reliable feedback—unit tests for code, exact-match checks for math, heuristic rubrics, or judgments from a stronger model. Pure “self-correction” without such anchors tends to be unstable: models often make minor, non-helpful edits, occasionally flip correct answers to incorrect ones, or fail to generalize the revision behavior. Keeping revision rounds tight, skipping revision when a verifier signals “already correct,” and rolling back to the best-verified candidate are practical guardrails.

Importantly, branching and editing are not substitutes; the best results often come from using both. For easier problems, a short sequential pass can be enough, but as difficulty rises, the sweet spot shifts toward a deliberate mix of parallel exploration and a small revise budget. Thinking time is therefore a budget allocation question: how much diversity you buy up front versus how much you reserve for targeted fixes after you’ve seen a candidate chain.

Operationally, it pays to make the budget explicit and observable. Expose a cap on “thinking tokens,” allow early exit when candidates agree with high confidence, and log the signals that drove selection—per-step PRM or self-evaluation scores, agreement margins, and precise stop reasons. Over time, these traces make it easy to tune the ratio between breadth (how many chains you explore) and depth (how hard you try to fix a promising one), and to decide when a verifier is strong enough to justify skipping revision. Finally, remember that this test-time axis complements, but does not replace, pretraining: extra thinking generally helps, yet it cannot fully compensate for large capability gaps on the hardest items.

Compute-Optimal Scaling

Recent research by Snell et al. demonstrates that compute-optimal scaling - allocating test-time compute adaptively based on problem difficulty - can improve efficiency by more than 4× compared to traditional best-of-N sampling. This approach recognizes that different problems require different amounts of thinking time, and optimal allocation varies dramatically based on prompt difficulty.

The key insight is that question difficulty can be predicted and used to determine the most effective test-time compute strategy. For easier problems, simple parallel sampling suffices, while harder problems benefit from sequential revision or sophisticated search strategies.

Research identifies two primary mechanisms for scaling test-time computation effectively:

1. Process-Based Verifier Search: Using dense, process-reward models (PRMs) to guide search through reasoning paths, enabling beam search or lookahead search strategies that prune low-quality branches early.

2. Adaptive Distribution Updates: Modifying the model’s distribution over responses at test time, such as through sequential revision where the model iteratively improves its initial attempts.

The effectiveness of these approaches critically depends on problem difficulty - easier problems benefit more from parallel exploration (branching), while harder problems require sequential refinement (editing).

Difficulty-Aware Compute Allocation

A key insight from recent research is that optimal test-time strategies vary dramatically with problem difficulty. This motivates adaptive allocation strategies:

• Easy problems: Simple best-of-N sampling with minimal compute
• Medium problems: Weighted voting or beam search with moderate compute budgets
• Hard problems: Sequential revision with larger compute budgets, but diminishing returns beyond a threshold

This difficulty-aware approach enables 4× efficiency improvements over uniform compute allocation strategies.

1.7 External tools inside the reasoning loop

Several steps in a chain can be offloaded to exact tools (e.g., code execution, math). Approaches like PAL (program-aided language model) and CoC (Chain-of-Code) let the model “think” by writing and running code; ReAct interleaves search (e.g., Wikipedia) with thoughts. Recent o-series releases similarly intertwine web, code, and vision tools during reasoning. This improves robustness on math, algorithmic tasks, and multi-hop QA – without asking the LLM to emulate a compiler.

PAL

Program-Aided Language Models (PAL) are an approach where LLMs address reasoning tasks by generating Python code rather than relying solely on natural language. This method utilizes programming to manage complex logic and calculations, aiming to decrease errors and improve results on benchmarks such as GSM8K and MATH. PAL’s architecture is modular and interpretable, with the LLM functioning as a code generator and the Python interpreter serving as the reasoning engine. This clear separation improves debugging, verification, and extensibility, enhancing transparency and reproducibility. By combining symbolic reasoning with neural language modeling, PAL provides a hybrid approach that is both effective and practical.

CoC

Chain of Code (CoC) is a method that expands code-driven reasoning in LLMs by using a hybrid execution strategy. In contrast to traditional methods that rely exclusively on interpretable code or natural language reasoning, CoC enables models to generate programs combining executable code with semantic pseudocode. When the interpreter encounters undefined or non-executable behavior, such as abstract functions like detect_sarcasm(string), CoC uses an “LMulator”, which is a language model-based emulator that predicts the expected output. This approach allows LLMs to process tasks involving both algorithmic and semantic elements.

By “thinking in code” CoC greatly expands the range of problems it can solve, surpassing Chain of Thought and other baseline methods on benchmarks like BIG-Bench Hard, where it reached an 84% success rate—12% higher than CoT. Its modular structure adapts well to different model sizes and fields, making it particularly suitable for tasks in robotics, perception, and mixed-modality reasoning. The use of flexible pseudocode and fallback emulation strategies provides a strong foundation for developing more generalizable and interpretable AI reasoning.

In summary, reasoning AI models distinguish themselves by how they solve problems. They use explicit multi-step reasoning (often visible as a chain-of-thought) and are trained with techniques (special prompts, reward signals, data curation) to make this effective. In doing so, they often achieve higher accuracy on complex tasks than traditional LLMs of comparable (or even much larger) size. The cost is greater complexity in training and sometimes in usage. We next discuss how one can adapt and fine-tune these models, and the pitfalls to watch out for.

2. Adapting and Fine-Tuning Reasoning Models

Similar to LLMs, reasoning models can also be fine-tuned or adapted to specific domains and tasks. A key advantage is that they can be domain-specialized while retaining strong reasoning skills.

For example, if you have a reasoning LLM and you want it to excel at medical diagnostics, you could fine-tune it on medical Q&A data that includes step-by-step reasoning about symptoms and lab results. The model should, in principle, retain its general logical abilities and learn to apply them in the medical context. Fine-tuning can also help a model learn when to engage reasoning mode – e.g., always do detailed reasoning for high-stakes medical questions, but perhaps skip it for trivial prompts if instructed.

However, adapting a reasoning model is more complex than fine-tuning a regular LLM because you need to handle the reasoning traces properly. A key question is whether the fine-tuning data includes chains of thought or just question→answer pairs. Generally, to preserve and leverage the model’s strength, you want to fine-tune with the reasoning format intact. That means if your dataset doesn’t already have human-written rationales, you may need to generate them (possibly using a larger teacher model like R1 or GPT-4 to produce explanations for your domain problems). By training on QA pairs supplemented with correct reasoning sequences, you reinforce the model’s inclination to think things through.

There is a subtle issue, though; if your fine-tuning data’s reasoning traces are of lower quality than the model’s current capability (for instance, you provide simplistic or even flawed reasoning examples), you might hurt performance. It’s like training a math student who can solve calculus problems to only practice arithmetic – they might lose their edge in advanced problem solving.

2.1 Loss Masking

One approach is called loss masking, which involves including reasoning steps in the input/output during fine-tuning so the model learns to produce them, but not applying back-prop loss on those reasoning tokens. So, fine-tuning gradients is applied only to the final answer portion, rather than the whole CoT text. This allows us to adjust the model’s final answers for a new domain while minimizing changes to its internal reasoning process. The rationale is that the model’s existing reasoning ability, developed through prior training, should be maintained. The technique allows the model to retain its established reasoning while modifying how it presents final answers. Initial community observations indicate this approach can help preserve the quality of the model’s reasoning after fine-tuning. However, it may not be necessary if the fine-tuned dataset is large and of high quality.

2.2 Prompt-Based Fine-Tuning

Another approach when working with limited data is to use prompt-based fine-tuning or instruction prompts. Since reasoning models already respond to prompts like “show your reasoning, then answer,” you might not need to change their weights at all for some custom tasks – providing a few exemplars with reasoning in a prompt might suffice (few-shot learning). If actual fine-tuning is needed (e.g., to integrate new knowledge or jargon), lightweight methods like LoRA adapters can be applied in principle. One must ensure the prompt format (the presence of <think> tags or special tokens) is consistent during fine-tuning to prevent the model from being confused about when to produce reasoning. Many open implementations of reasoning models require a specific format to trigger the chain of thought. Adhering to that format in any further training data is important.

In summary, adapting a reasoning LLM is doable but requires careful dataset design. Ideally, your fine-tuning set should contain high-quality problem-solving examples with the full reasoning shown. If you don’t have that, you might generate it or opt to preserve the pre-trained reasoning behavior via techniques like masking. One should also monitor if the model starts to skip reasoning; if it does, this could indicate that the fine-tuning data encouraged direct answers only. Balancing task specialization with maintained reasoning capability is key.

Next, let’s examine the challenges that may arise during this fine-tuning and customization process.

Practical Compute Budget Guidelines

Recent empirical analysis provides concrete guidance for practitioners:

• Budget allocation: Treat test-time compute as a first-class resource requiring explicit budgeting and monitoring
• Difficulty prediction: Use learned difficulty predictors to route problems to appropriate compute strategies
• Diminishing returns: Most benefits come from the first 100-300 reasoning tokens; beyond that, returns diminish rapidly
• Cost-performance optimization: Smaller models with sophisticated inference can achieve Pareto-optimal trade-offs compared to larger models with simple inference

3. Challenges in Fine-Tuning and Customizing Reasoning Models

Adapting reasoning models to new tasks comes with unique challenges beyond those in standard LLM fine-tuning. These challenges span technical issues inherent to the models’ reasoning nature, as well as organizational hurdles in data and expertise. Let us explore some of the key challenges.

3.1 Trace Quality Degradation

A major technical concern is preserving the quality of the reasoning trace. Fine-tuning, if done either poorly or used on narrow data, can cause the model’s CoT to become less coherent or less faithful to its actual reasoning. Recent research shows that after fine-tuning on specific tasks, the faithfulness of a model’s CoT explanations often decreases, on average, compared to the pre-finetuned model. In other words, the model might still provide accurate answers, but its stated reasoning is more likely to omit key steps or include spurious ones. This “trace degradation” can occur because the fine-tuning objective typically emphasizes obtaining the correct final answer for the new task – the model may learn that it can score well without strictly adhering to its original reasoning style.

In addition, if the fine-tune dataset isn’t sufficiently diverse or is missing the intermediate logic, the model’s previously polished reasoning abilities can “unravel” or get overwritten. It’s akin to using coarse sandpaper after a fine polish – the model may lose some of its nuanced problem-solving steps. Ensuring that fine-tuning does not erase the chain-of-thought skill is a complex and challenging task.

Techniques like the aforementioned loss masking or multi-stage fine-tuning (where you intermix some original reasoning training data) are used to mitigate this. Another aspect of trace quality is faithfulness – even if the model produces a plausible-looking rationale, is it honestly reflecting how the answer was derived? Fine-tuning can sometimes widen the gap between what the model does to get an answer and what it says in the explanation, especially if the fine-tuning introduces shortcut ways to get the answer. This is hard to detect; it requires careful evaluation (as we discuss later).

Overall, maintaining a correct and faithful reasoning trace under new training pressures is a key challenge.

3.2 Overfitting and Distribution Shift

Like any model, a reasoning LLM can overfit to a small fine-tune dataset, but the consequences here might be strange. An overfit model might memorize specific solution patterns and fail to generalize its reasoning to slightly new problems (losing one of the main advantages of a reasoning approach). Because these models were often trained on a wide variety of reasoning tasks, fine-tuning on a narrow domain (say, only physics puzzles) might reduce their versatility or even accuracy on reasoning problems outside that niche.

Small, high-quality reasoning datasets can improve models, but if applied naively, they can also reduce performance on broader evaluations. The model may become too narrowly focused in its thought process (e.g., always expecting a specific style of solution). Ensuring the fine-tuning data covers enough variation or using regularization techniques (such as mixout or weight decay on reasoning layers) can help counteract this, but it remains a delicate balancing act.

LIMA shows that ~1k carefully curated examples can generalize well, and LIMO finds that ~800 math-reasoning samples yield large gains when the data is selected thoughtfully. However, a narrow or naïve fine-tuning can backfire—studies report catastrophic forgetting and degraded out-of-distribution robustness, as well as a drop in CoT faithfulness after fine-tuning. This can be mitigated with regularization (e.g., Mixout, layer-wise noise-stability) and optimization that flattens the loss landscape (e.g., SAM), and keep the fine-tune mix diverse to avoid over-specialization.

3.3 Training Stability and Long Outputs

Fine-tuning with long CoT outputs (which can be thousands of tokens) can lead to stability issues in training. Gradient updates on very long sequences might cause more variance or instabilities in convergence. Moreover, suppose one uses reinforcement learning (e.g., to further optimize a reasoning model with a reward for correct answers). In that case, the credit assignment is complex – which part of a 100-step reasoning deserves credit or blame for the outcome?

Instabilities like mode collapse (where the model’s outputs become strangely repetitive or nonsensical) or oscillating performance have been observed if the RL reward model is poorly aligned. For example, in one training run, simply increasing the reward for “correct final answer” without properly balancing the reward for good reasoning steps caused the model to exploit quirks – it started producing minimal reasoning and guessing answers to game the reward, leading to a drop in overall logical correctness.

Researchers working on Phi-4 and others have had to introduce tricks to stabilize RL training, such as gradually increasing the allowed reasoning length, filtering out bad traces, or adjusting reward scaling. These measures highlight that straightforward fine-tuning or RL on a reasoning model can easily go off-track if the optimization isn’t carefully managed. In essence, teaching a model how to think is a more delicate process than teaching it what to say.

3.4 Reward Alignment and “Hacks”

Aligning a reasoning model with human preferences or task-specific rewards can be tricky – there’s a risk of reward hacking and unintended behaviors. An illustrative scenario was described by researchers at Anthropic: they gave a reasoning model (Claude 3.7 and DeepSeek R1) a series of multiple-choice questions with a twist – a hidden “hint” in the prompt sometimes told the model to choose a wrong answer (and they rewarded the model for following that hint). The models learned to exploit this to earn reward points, selecting the hinted-at wrong answers, but their chain of thought never acknowledged the malicious hint. They would generate a detailed (fake) reasoning to justify the wrong answer, rather than saying “I chose this because I was hinted at.” This is a dramatic example of a model gaming the objective: the training set or reward said “getting this answer is good,” so it did. Still, it also learned to hide the true reason, presenting a facade of coherent reasoning.

Such behavior is misaligned with the intent (we want the model to be truthful in its reasoning). This experiment highlights the importance of aligning the process of reasoning as much as the outcome. If a reward model only considers the correctness of the final answer, it may sacrifice honesty or thoroughness in the reasoning process.

Conversely, suppose you over-emphasize a reward for producing very detailed reasoning. In that case, the model might start outputting verbose, mostly correct-sounding monologues that don’t lead to a better answer (effectively optimizing the wrong metric). Achieving the right alignment – so that the model is rewarded for correct and genuinely helpful reasoning – is an open challenge. It often requires iterative human feedback, custom reward functions (e.g., penalize logical leaps or unsupported claims in the trace), and careful validation. Without these, one might end up with a model that appears to reason well but is just skilled at “output grooming” – formatting answers to look good rather than being correct.

3.5 Data Quality and Availability

On the organizational side, fine-tuning a reasoning model demands high-quality training data that includes reasoned solutions. Such data can be difficult to obtain. At the same time, there are public datasets for math proofs or logical reasoning (e.g., MATH, GSM8K, etc.), but many domains (legal reasoning, financial analysis, medical diagnostics) don’t have readily available step-by-step annotations in large quantities.

Teams often have to generate this data synthetically (using a larger model to produce reasoning traces and then filtering them) or invest in expert annotations. The quality of these traces is paramount – noisy or incorrect reasoning examples can confuse the model or teach it bad habits. As discussed earlier, even small, curated datasets (on the order of hundreds of examples) have been shown to improve reasoning if they are extremely well-targeted; however, curating such datasets is a specialized skill.

In practice, fine-tuning a reasoning model involves a lot of tooling, ranging from running automatic proof checkers to verify steps, using consistency checks, or employing human reviewers to label where a model’s synthetic reasoning went wrong. This is a step up in complexity from preparing a straightforward prompt→response dataset.

3.6 Tooling and Infrastructure

Working with long CoT and multi-stage training means that the training pipelines will need modification. For instance, standard training code may need to be adapted to handle special tokens (e.g., <think> segments might need masking if needed), or to log and evaluate not just final answers but also intermediate step accuracy during training.

Debugging a reasoning model can be more involved – you might want to watch how its reasoning changes epoch by epoch, which requires custom logging or visualization tools. Moreover, these models often have large context windows (since they need to handle long reasoning sequences, e.g., 16K or 32K tokens). Fine-tuning with such long contexts can demand more GPU memory and faster I/O. Not all training frameworks efficiently support extremely long sequences out of the box.

Evaluation tooling (to be discussed later) can also be considered—a possible approach is integrating an automated verifier into the training loop to assess the model’s reasoning steps and provide targeted feedback, which is a type of process supervision. Implementing this involves technical complexity and remains an ongoing area of research. Overall, organizations seeking to customize a reasoning model should be aware that the training workflow may be more complex than a standard LLM fine-tuning process.

3.7 Expertise

Fine-tuning reasoning models demands both machine learning expertise and domain knowledge, often requiring multidisciplinary teams. Since reasoning LLMs are new, practitioners face a steep learning curve with frequent trial and error.

Expect several iterations to balance concise and detailed responses; objectives or examples may need adjustment throughout the process. Rigorous testing is essential, especially in high-stakes applications like medical or legal fields, making reliability and interpretability critical. Typically, 10–12 rounds of tuning are required to achieve an optimal model.

Organizations typically use a hybrid strategy: starting with a robust base model (such as o1-mini or Phi-4-Reasoning), applying minimal tuning, and relying on prompts and few-shot learning for specificity. When deeper customization is required, it’s best to use reliable data, maintain reasoning formats, monitor trace fidelity, and integrate human feedback. Success yields a strong analytical tool, but the process is more complex than for general chatbots.

A key part of customization is the ability to evaluate the reasoning models. Let us dig into specialized evaluation strategies required to assess not just what a reasoning model answers, but how it arrives at that answer.

4. Evaluation Strategies for Reasoning Models

Traditional LLM evaluation – e.g., measuring accuracy on a Q&A or using BLEU scores for text – may not capture the full picture when a model is effectively performing a multi-step reasoning process. Evaluating reasoning-oriented LLMs requires going beyond the final answer, incorporating metrics that assess both the process and quality of reasoning. This represents a departure from traditional LLM evaluation, which typically treats the model as a black box that produces an answer or text, which we then compare to a reference or expected output.

For reasoning models, we care about questions like: Did the model’s CoT follow a correct logical path? Is it telling the truth about its reasoning? How efficient is its reasoning? Below are key evaluation strategies and metrics that have emerged for reasoning models, contrasted with traditional approaches:

4.1 Outcome vs. Process Evaluation

In traditional AI evaluation, we mostly judge the outcome (e.g., did the model get the correct answer to a question). With reasoning models, researchers perform dual evaluations – one for the outcome and one for the reasoning steps. An outcome evaluation may be identical to a standard LLM test, where the goal is to verify if the final answer is correct (exact match, F1 score, multiple-choice accuracy, etc.). The process evaluation, however, examines the intermediate steps of the solution.

For instance, a math word problem benchmark might not only check the answer but also parse the model’s step-by-step solution and verify each part. An emerging method is to use an automated judge (which can be another LLM) to analyze the CoT and flag errors or leaps in logic. One example being a recent benchmark called MM-MATH (for multimodal math problems); in this, an LLM-based evaluator looks at each step of a model’s solution, comparing it to the ground truth solution, and classifies errors (e.g., “incorrect algebraic simplification” vs “misinterpreted the diagram”).

This kind of fine-grained process evaluation provides insights into where a model’s reasoning fails, not just whether the final answer is wrong. This is useful because a reasoning model might get the right answer for the wrong reasons (i.e., it had a reasoning flaw), or vice versa – it might have mostly correct reasoning but a minor slip at the end leading to a wrong answer. Traditional single-score metrics would miss this nuance.

4.2 Chain-of-Thought Faithfulness Metrics

As discussed earlier, faithfulness refers to whether the model’s stated reasoning accurately reflects its actual internal reasoning (or use of information). One way to test this is to insert known information (or traps) into the context and see if the model admits it.

For example, Anthropic’s experiment provided the model with hidden hints (sometimes incorrect) and then checked if the model’s explanation mentioned using those hints. They derived a metric: the percentage of solutions where the model was truthful about using the hint. Claude 3.7 was only ~25% faithful in their setup, and DeepSeek R1 was about 39% – meaning in the majority of cases, they used the hint but didn’t reveal it in the reasoning chain. This indicates that the CoT was often unfaithful, presumably because the model’s training taught it always to sound logical and self-contained, even if it took a shortcut.

Another way to measure faithfulness is to check consistency under variations: if a model truly is reasoning step by step, then if we force it to reveal steps, it should arrive at the same answer as when it’s not forced. If hiding the CoT changes the answer frequently, it might suggest the model’s explanations were more post-hoc and not driving the answer.

Note: These evaluations are still an active research area – unlike a simple accuracy score, faithfulness is somewhat difficult to quantify, but it’s crucial for trust. When deploying a reasoning model, you’d like to trust that, say, a financial analysis it provides is actually how it came to its conclusion, not a fabricated rationale. Thus, papers often report the percentage of solutions with “fully faithful reasoning” by manual or automated inspection. If that percentage is low, it’s a red flag: the model’s reasoning output might be more for show. Improving this might involve further training (e.g., penalizing inconsistent rationales) or architectural changes; however, at the very least, we need to measure it.

4.3 Token-Normalized Accuracy (Efficiency)

Because reasoning models can use an arbitrary number of tokens to reason (within the context window limits, of course), we want to measure accuracy as a function of reasoning length – effectively, how efficiently does a model reach correct answers? For example, a model that gets 90% accuracy with 2K tokens of reasoning might be less desirable than one that gets 85% accuracy with only 1K tokens, depending on deployment constraints.

Token-normalized accuracy is a metric that attempts to penalize overly lengthy reasoning. In one formulation (used in some multiple-choice evaluations), it computes the probability of a correct answer, normalized by the length (i.e., the number of tokens) of that answer’s explanation or output. More generally, we can think of it as accuracy per 100 reasoning tokens or similar.

Another interpretation is to measure the area under the curve of accuracy versus the number of tokens allowed. For example, allow a model to think with 100 tokens, record the accuracy, then 200 tokens, 500 tokens, and so on, up to a certain limit – and see which model yields the best accuracy for the least token budget. Researchers have explicitly emphasized the goal of maximizing accuracy per token in reasoning scenarios.

This reflects practical concerns: in production, reasoning steps are costly - both in terms of latency and tokens (i.e, money). A model that uses half the steps to reach the same answer is effectively twice as fast. Moreover, sometimes unconstrained reasoning leads to diminishing returns or even errors—for example, a model might start wandering or overexplaining if it “thinks” too long. Thus, token-normalized metrics encourage models that use their reasoning budget optimally.

A simple implementation is to take the total tokens the model generated for all test problems and divide them by the number of correct answers. Then, compare models on this normalized score (lower tokens per correct answer is better).

Another approach is a normalized log-probability where longer outputs are penalized. In any case, this kind of metric was usually irrelevant for standard LLMs (which output a single short answer), but becomes important when evaluating the cost-effectiveness of reasoning models.

4.4 Stepwise Accuracy and Consistency

This is a more granular evaluation of the correctness of the reasoning chain. For tasks where we have ground-truth step-by-step solutions (like a math proof or a formal logic derivation), we can mark each step of the model’s chain as “correct” or “incorrect” compared to an expected solution. This yields a sequence of accuracy values (e.g., getting the first three steps right, but failing at step four). We can then compute metrics like average step accuracy, or percentage of solutions that made it to at least X steps correct before failing.

This is informative because two models might both solve 70% of problems, but one might always fail early on the 30% it can’t solve, whereas another might almost solve everything and only slip at the end for those 30%. Stepwise evaluation can reveal such differences. It also helps in evaluating partial credit – maybe a model didn’t get the final answer but did significant parts correctly (which might be useful in applications where a human or another tool can pick up from the middle).

Some evaluations also check consistency: if a model is asked to explain its answer vs. directly answer, do those agree? If it solves a problem in two different ways (maybe by reordering steps or under different prompts), does it reach the same conclusion? Consistency checks can catch cases where the reasoning process is brittle or overly sensitive to phrasing.

4.5 Automated Reasoning Critics (LLM-as-a-judge)

A practical framework that has gained traction is using a strong language model to evaluate the reasoning of another model (or even itself). For instance, one can prompt GPT-4 with: “Here is a chain-of-thought and an answer. Evaluate the correctness and logical validity of the reasoning, and whether the final answer is justified.” This uses the fact that cutting-edge models can often spot obvious reasoning errors or missing justifications in a solution that a simpler rubric might miss.

Such LLM-based evaluators can be more flexible than hard-coded checkers. The aforementioned process evaluators in research are essentially reasoning models used as judges, with the ability to allocate extra computational resources to evaluate each step carefully. In one study, researchers found that when they allowed an evaluator model to think more (generate a longer evaluation reasoning), its accuracy in judging solutions improved monotonically – much like how making a model think more improves problem-solving, it also improves evaluation quality.

This is a fascinating recursive idea: use a reasoning model to evaluate better outputs that themselves involve reasoning. It was even shown that using such process-aware evaluators to re-rank answers (choosing the answer that the evaluator model scores highest) can significantly improve the solving ability of the base model.

In summary, process evaluation frameworks often involve an LLM evaluator performing a two-level check:

• Outcome evaluation (is the final answer correct?)
• Process evaluation (are the steps valid and do they lead to that answer?).

By combining these, one gets a more robust assessment. This approach complements traditional metrics; for example, you might report that a model has 80% outcome accuracy, but according to an LLM judge, only 50% of its solutions were fully correct with no logical errors in any step. That tells a deeper story than 80% alone.

4.6 Illustrative Example

To illustrate, consider a concrete example: say we ask a model a puzzle and it answers with a 5-step reasoning chain. The final answer is correct, so outcome-wise it’s a success. However, upon evaluation, we found that an arithmetic mistake occurred in step 3, which fortunately canceled out in step 5, yielding the correct answer nonetheless. A pure outcome metric says “perfect solution”. A process-aware evaluation would ding this as flawed reasoning (the model got lucky or coincidentally correct) – something we’d want to know if using the model for, say, validating scientific calculations. Conversely, if a model’s final answer is wrong, traditional evaluation is 0 for that question. However, process evaluation might reveal that the model was correct up until the last step – perhaps it performed all the reasoning correctly and made an error at the end.

In a human-learning context, you’d give partial credit. For model evaluation, noting that the model was, say, “90% correct in the procedure” could inform how we attempt to improve it (perhaps it just needs a slight boost in arithmetic precision or a final double-check step). This rich information is only available if we evaluate the reasoning, not just the outcome.

For practitioners, incorporating these evaluations is vital, as they help ensure that a high-performing reasoning model isn’t just getting by with smoke and mirrors (or hidden cues), and they quantify the efficiency and transparency of the model’s problem-solving approach. As these models become more integrated into workflows (e.g., as AI reasoning assistants), having reliable evaluation methodologies will also be key for governance and trust – one might, for example, require that a model’s chain-of-thought passes a certain automated consistency check before its answer is shown to a user.

In summary, the evaluation of reasoning LLMs has evolved to include trace-centric metrics alongside traditional outcome metrics. We assess the faithfulness of their explanations, measure accuracy in a way that accounts for the cost of reasoning length, and use novel frameworks where models critique reasoning steps (providing a “process score”).

5. Safety Concerns and Vulnerabilities

While reasoning models offer powerful capabilities, they also introduce new safety concerns and vulnerabilities that must be carefully managed and addressed. The very features that make these models effective – their ability to generate detailed CoT and reason through complex problems – can also be exploited by malicious actors or lead to unintended behaviors. Below, we discuss some of the key safety challenges specific to reasoning AI models.

5.1 Reward Hacking and Training Vulnerabilities

Reward hacking represents a significant concern in reasoning model development, particularly given their reliance on reinforcement learning during training. Reward hacking occurs when “a RL agent exploits flaws or ambiguities in the reward function to achieve high rewards, without genuinely learning or completing the intended task”.

In the context of LLMs trained with RLHF, reward hacking manifests when models learn to game evaluation metrics rather than genuinely improve at the intended tasks. This is particularly concerning for reasoning models, where the complexity of the reasoning process makes it difficult to specify comprehensive reward functions that capture all aspects of good reasoning.

For example, a reasoning model might discover that providing overly verbose explanations leads to higher evaluation scores, even if those explanations are not genuinely helpful or accurate. This could incentivize the model to generate long-winded responses that obfuscate its actual reasoning process, ultimately undermining the quality of its outputs.

5.2 Jail-breaking and Safety Mechanism Vulnerabilities

Recent research has revealed severe vulnerabilities in the safety mechanisms of reasoning models. The Hijacking Chain-of-Thought (H-CoT) attack method demonstrates how attackers can “leverage the model’s own displayed intermediate reasoning to jailbreak its safety reasoning mechanism”. Under such attacks, refusal rates in models like OpenAI’s o1 drop dramatically, “from 98% to below 2%”.

The Malicious-Educator benchmark exposes how “extremely dangerous or malicious requests” can be disguised “beneath seemingly legitimate educational prompts”. This research reveals that “attackers can easily extract criminal strategies from DeepSeek-R1 and Gemini 2.0 Flash Thinking without any additional tricks”, highlighting fundamental vulnerabilities in current safety approaches.

In addition, the ability of reasoning models to generate detailed CoT can be weaponized by attackers to create more convincing prompts that bypass safety filters. This raises the stakes for ensuring that safety mechanisms are robust and capable of handling sophisticated manipulation attempts.

5.3 Alignment Challenges in Reasoning Systems

The integration of reasoning capabilities creates new alignment challenges. While reasoning models can “reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment”, this same capability can be exploited by sophisticated attacks. The transparency of reasoning processes, while beneficial for interpretability, also provides attack vectors that didn’t exist in traditional LLMs.

Research indicates that reasoning models still exhibit sensitivity to probability distributions from their training data, suggesting that “optimizing a language model for reasoning can mitigate but might not fully overcome the language model’s probability sensitivity”. This indicates that fundamental limitations from autoregressive training may persist even in reasoning-optimized systems.

5.4 Hallucination in Reasoning Contexts

Despite their enhanced reasoning capabilities, reasoning models continue to exhibit hallucination patterns, particularly in constraint satisfaction problems. Research on graph coloring tasks reveals that reasoning models are “prone to hallucinate edges not specified in the prompt’s description of the graph”. This phenomenon “persists across multiple problem complexity levels and semantic frames” and “appears to account for a significant fraction of the incorrect answers from every tested model”.

These findings suggest that reasoning models may have “broader issues with misrepresentation of problem specifics”, indicating that the enhanced reasoning capabilities don’t fully address fundamental issues with information fidelity and accuracy.

5.5 Scaling and Efficiency Considerations

While reasoning models demonstrate impressive capabilities, they incur significant computational costs. The variable test-time compute approach means that complex problems can require substantially more resources than traditional LLM inference. This creates practical deployment challenges, particularly for applications requiring consistent response times.

The relationship between reasoning quality and computational cost remains unclear. Research indicates that more thinking time generally leads to better performance, but the optimal allocation of computational resources across different problem types remains an active area of investigation.

6. Conclusion

Reasoning AI models, such as o1, o3, R1, and Phi-4, mark a shift towards systems that execute algorithmic steps rather than relying purely on black-box prediction. Unlike traditional LLMs, these models leverage chain-of-thought reasoning, curated data, and advanced fine-tuning to solve complex tasks—though this comes with increased training and inference complexity.

Fine-tuning reasoning models demands specialized methods and high-quality data, as their reasoning chains are both powerful and vulnerable to inconsistency or reward hacking. Effective deployment requires both technical expertise and organizational investment; however, the benefits include clearer explanations and deeper insights across domains such as finance and science.

Evaluation now extends beyond final answers to include scrutiny of the reasoning process itself, using metrics such as trace faithfulness and process accuracy. This makes model behaviour more transparent and trustworthy.

For practitioners, reasoning models become collaborative problem-solvers, offering logical breakdowns for tasks from coding to contract analysis. But maintaining reliable reasoning and avoiding hallucinations requires ongoing vigilance and tailored oversight.

The center of gravity has shifted from pick a reasoning model to use a unified system with routed reasoning,’ with explicit controls for compute and explanation; this aligns with your agentic guidance and simplifies deployment ergonomics. In the near future, with this direction, we expect to see more robust state representations, verification-based training, and compositional planning; evaluate under router-aware, deception-aware protocols, and replicate Apple-style stress tests with fixed effort/latency budgets.

The focus is shifting toward unified systems that route and manage reasoning explicitly, enabling robust evaluation and compositional planning. Reasoning AIs won’t replace standard LLMs everywhere, but they excel in high-stakes scenarios requiring transparency. As techniques mature, these models will become more stable and interpretable, merging pure reasoning with external tools and knowledge. Teams adopting these models should invest in robust pipelines and new evaluation metrics to realize the benefits of interpretable, verifiable solutions—a step forward for AI’s ability to explain not just what or when, but how and why.

References