Building and Refining AI Reasoning Models: A Literature Review

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Table of Contents

1. Definition of Reasoning Models

2. When to Use Reasoning Models

3. DeepSeek-R1: Training Pipeline and Reasoning Optimization

4. Four Key Methods to Build and Improve Reasoning Models

   • Inference-Time Scaling (Test-Time Reasoning Enhancements)

   • Pure Reinforcement Learning (RL-Only Training)

   • Supervised Fine-Tuning + Reinforcement Learning (Hybrid RLHF Approaches)

   • Pure Supervised Fine-Tuning and Distillation

5. Industry Applications of Reasoning Models

6. Budget Considerations for Building Reasoning Models

Understood. I will conduct a comprehensive literature review covering the most recent arXiv papers (2024-2025) on reasoning models, focusing on methods and strategies for building and refining these models. The analysis will be broad across various industries, covering both enterprise-scale and resource-constrained development environments.

I will also provide an in-depth examination of the DeepSeek training pipeline and DeepSeek R1, along with industry applications from official sources such as PyTorch, TensorFlow, and other frameworks. Expect inline citations for all sources.

I will notify you when the research is complete.

Definition of Reasoning Models

Reasoning models in AI are advanced language models specifically trained to “think” through complex problems step-by-step before producing an answer (R1 is reasoning for the masses - by Charlie Guo). Unlike standard large language models (LLMs) that often generate a response in one pass, reasoning models employ an internal chain-of-thought (CoT) – a series of intermediate reasoning steps – much like a human’s thought process . For example, OpenAI’s o1 model family pioneered this approach by breaking down problems into multiple steps and refining their thinking before finalizing an answer . This means a reasoning LLM will silently work through sub-problems or hypotheses (which can sometimes be shown as a “thought process” if enabled) and possibly correct itself along the way . By performing deeper multi-step analysis, reasoning models can tackle tasks that require logic, planning, or multi-hop inference, rather than relying purely on surface-level pattern matching from training data . In essence, a reasoning model is a type of LLM “that can perform complex reasoning tasks,” distinguishing itself by its structured problem-solving approach (Articles by Alex Woodie's Profile | BigDATA Wire, IT Jungle Journalist).

Key characteristics of reasoning models include:

• Step-by-step problem solving: They generate explicit or latent intermediate steps (a CoT) instead of jumping straight to an answer . This makes their solutions easier to verify, as they often explain themselves step-by-step .

• Self-correction and reflection: They can recognize when an intermediate step seems wrong and revise it (a capability not present in basic LLMs) . This iterative refinement leads to more reliable outcomes on complex tasks.

• Deeper reasoning beyond training data: By reasoning, they can combine learned knowledge with logical deduction. This helps them solve puzzles or questions in ways that go beyond memorized responses, addressing problems that stump “shallow” LLMs .

In summary, reasoning models extend the power of LLMs by incorporating an internal thinking loop. This allows them to handle tasks requiring logical sequencing, long-term planning, or multi-step reasoning that ordinary generative models struggle with. As one commentator put it, reasoning models “employ an internal reasoning process that mirrors human trains of thought” rather than blurting out an immediate answer (R1 is reasoning for the masses - by Charlie Guo).

When to Use Reasoning Models

Because of their ability to handle complex, multi-step reasoning, these models are essential in scenarios where straightforward question-answering or text generation is insufficient. You would turn to a reasoning model when a task requires planning, logical deduction, or chain-of-thought analysis to reach a correct solution (Exploring Reasoning LLMs and Their Real-World Applications) . Key scenarios include:

• Complex Problem Solving: For mathematical proofs, multi-step math word problems, or scientific reasoning, reasoning LLMs excel by working through each step. For instance, OpenAI’s o1 and other recent “reasoners” can solve complex math or logic puzzles that earlier models would get wrong without stepwise thinking . If a question involves reasoning through several layers of conditions (like a puzzle or an Olympiad geometry problem), a reasoning model is far more likely to reach a correct answer than a standard LLM.

• Long-form Logical Queries: In domains like law or analytical finance, a single question may require analyzing multiple facts or regulations in sequence. Reasoning models can break down a query (e.g., a legal question that needs applying several statutes) into sub-queries and deduce an answer in a logical progression. They are also useful for theorem proving or software verification, where formal logical steps are needed .

• Planning and Decision Support: Reasoning LLMs shine when used as planners or decision aids. Rather than just answering a question, they can plan a series of actions. For example, in an autonomous agent setting, a reasoning model can decide: “To accomplish task X, I should do step 1, then step 2, then step 3,” and so on. This is crucial in applications like robotics (for task planning), or scheduling and optimization problems, where decomposing a high-level goal into actionable steps is required (Large Action Models (LAMs): Applications and Challenges) . Regular LLMs lacking deep reasoning might skip such planning and produce incomplete solutions.

• Ambiguous or Novel Problems: If facing questions that aren’t straightforward or were never directly seen in training data, reasoning models attempt to generalize by reasoning. A non-reasoning model might just make a guess based on similarity to known data, whereas a reasoning model will try to logically figure it out. This makes them valuable for anything requiring on-the-fly reasoning, e.g. debugging code by considering why an error occurs and iterating through hypotheses, or handling complex customer queries that involve several related questions at once.

In short, whenever a task involves multi-step thinking, intermediate decisions, or the need to verify and correct along the way, a reasoning-oriented model is the appropriate choice. They are less needed for simple tasks (like straightforward fact recall or single-sentence completions), where a standard LLM is often sufficient. But for puzzles, long-form reasoning, and critical decision making tasks, these models are essential to reach accurate and reliable outcomes (Exploring Reasoning LLMs and Their Real-World Applications). Indeed, benchmarks have shown that reasoning LLMs vastly outperform older models on complex reasoning challenges – confirming their necessity for those scenarios .

DeepSeek-R1: Training Pipeline and Reasoning Optimization

DeepSeek-R1 is a recent open-source reasoning model that provides a case study in how to train and optimize an LLM for improved reasoning capabilities (deepseek-ai/DeepSeek-R1 · Hugging Face) . Developed by the company DeepSeek (a newcomer in 2023), R1 garnered attention for achieving reasoning performance on par with some of OpenAI’s models (like o1) while being fully open-source . This did not happen by accident – it was the result of a carefully designed training pipeline geared towards efficiency and reasoning.

Multi-Stage Training Pipeline: DeepSeek-R1 was trained through a four-stage pipeline that alternated between supervised and reinforcement learning phases to progressively build the model’s reasoning skill . In summary, the stages were:

1. “Cold-Start” Supervised Fine-Tuning (SFT) – DeepSeek’s team first collected a few thousand high-quality reasoning demonstrations (chain-of-thought examples) and fine-tuned their base LLM (called DeepSeek-V3-Base) on this data (DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning). This gave the model a seed of reasoning ability and improved output readability. Even a small amount of supervised training on step-by-step solutions helps the model avoid gibberish or chaotic outputs and establishes some basic reasoning patterns. The DeepSeek authors found this step crucial to address issues encountered when doing pure RL (described below), providing the model with a “common sense” starting point (deepseek-ai/DeepSeek-R1 · Hugging Face).

2. Reinforcement Learning Stage 1 (Reasoning-Oriented RL) – After the initial SFT, they applied large-scale pure Reinforcement Learning on the model to push its reasoning performance much further . Using a custom RL algorithm (GRPO) and only reward signals for correct reasoning/task success, the model was encouraged to explore and generate long chain-of-thoughts to solve complex problems. The result of this stage was a model dubbed DeepSeek-R1-Zero, which demonstrated remarkable reasoning behaviors emerging from the RL training alone . Notably, R1-Zero learned to perform self-verification and reflection – for example, it could generate a solution and then internally check its work and correct mistakes without any supervised examples of that process . This was a breakthrough result: it validated that pure RL (with no supervised data) can indeed cultivate reasoning capabilities in an LLM . DeepSeek-R1-Zero’s reasoning skill soared on benchmarks (e.g., its accuracy on a medical exam dataset jumped from 15.6% to 71.0% after RL training, an enormous gain) . However, R1-Zero also suffered some side effects: without any supervised grounding, it sometimes produced endless repetitive text, mixed languages, or poorly readable answers . These issues likely arose because the model was chasing the reward (solving the problem) at the expense of fluency or adherence to instructions. Thus, while Stage 2 unlocked reasoning power, it needed refinement to be user-friendly.

3. Supervised Fine-Tuning Stage 2 (Rejection Sampling & Broader Alignment) – To refine the RL-trained model, DeepSeek next generated a new supervised dataset by leveraging R1-Zero itself. They used rejection sampling: having R1-Zero solve many prompts, then filtering for high-quality reasoning outputs, which were added to a training set . They also incorporated some additional supervised data from their earlier model (DeepSeek-V3) covering general abilities like writing, factual Q&A, etc., to ensure the model retained a well-rounded skill set . The base model was then fine-tuned again on this mixture of data. This step effectively aligned the model with human preferences (via picking lucid, correct solutions from the RL model) and improved its general capabilities beyond pure reasoning. Fine-tuning on the RL model’s best outputs addressed the readability and repetition problems by explicitly training the model to produce solutions that were both correct and well-written .

4. Reinforcement Learning Stage 2 (Final Tuning) – In the last stage, they performed another round of RL on the newly fine-tuned model, this time using a wide range of prompt types (“prompts from all scenarios”) . The idea was to fine-tune via RL across diverse tasks – not just math or logic puzzles, but also coding, knowledge questions, etc. – to ensure the reasoning improvements generalize to all types of queries. This final RL pass yielded the ultimate model called DeepSeek-R1, which achieved performance on par with OpenAI’s o1 model (specifically, matching an OpenAI-o1 model’s score on benchmarks in December 2024) . The multi-stage approach allowed DeepSeek-R1 to combine the strengths of both supervised learning and pure RL, resulting in a highly capable and balanced reasoning model.

Efficiency and Results: One notable aspect of DeepSeek’s pipeline is that it was relatively compute-efficient given the gains achieved. Rather than training a new giant model from scratch, they started from a pre-trained base and focused on post-training techniques (SFT and RLHF) which require “minimal computational resources compared to pre-training” . This approach of heavy post-training paid off – DeepSeek-R1’s reasoning performance on complex tasks (math, coding, scientific QA) is comparable to closed-source state-of-the-art models . For example, through RL and a bit of voting at inference, DeepSeek-R1-Zero was able to reach an 86.7% success rate on a challenging medical exam benchmark, matching OpenAI’s o1 model . The final DeepSeek-R1 further improved generality and matched an even newer OpenAI-o1 variant . All of this was achieved in a matter of months, demonstrating how an optimized training pipeline can yield frontier-level reasoning ability at a fraction of the traditional training cost. Moreover, DeepSeek open-sourced not only R1, but also six distilled smaller models derived from it . These distilled models (ranging from 1.5B to 70B parameters) retain much of R1’s reasoning skill, making it accessible to those with lower compute – a point we revisit under Budget Considerations.

In summary, DeepSeek-R1 illustrates how multi-stage training with alternating Supervised Fine-Tuning and Reinforcement Learning can be used to efficiently build a reasoning model. By first seeding the model with some supervised knowledge, then letting it improve itself via RL (self-discovery of reasoning), and finally aligning it with human-like outputs, R1 achieved a high level of reasoning performance. This case study will inform several of the methods discussed next, as it actually combined all four of the key strategies for refining reasoning models (inference-time techniques, pure RL, SFT + RL, and distillation) into one coherent pipeline.

Four Key Methods to Build and Improve Reasoning Models

Researchers and practitioners have explored multiple strategies to enhance the reasoning capabilities of AI models. The most prominent methods can be categorized into four groups: (1) Inference-Time Scaling techniques, (2) Pure Reinforcement Learning, (3) Supervised Fine-Tuning combined with Reinforcement Learning, and (4) Pure Supervised Fine-Tuning & Knowledge Distillation. Each approach has its advantages and trade-offs. We review each method below, with recent research insights (2024–2025) illustrating how they contribute to building better reasoning models.

1. Inference-Time Scaling (Test-Time Reasoning Enhancements)

Inference-time scaling refers to improving a model’s reasoning performance without changing the model’s parameters, by giving it more “thinking time” or by using smarter decoding strategies at inference. OpenAI’s o1 series models introduced a simple but effective form of this: increasing the length of the Chain-of-Thought the model is allowed to produce (DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning). By prompting the model to generate longer, detailed reasoning chains before final answers, they achieved significant improvements on tasks like math, coding, and scientific Q&A . In essence, rather than restricting the model to a brief answer, you let it think out loud extensively, which often leads to more accurate conclusions.

Another inference-time technique is self-consistency via multiple sampling. Instead of one shot, you sample the model’s answer multiple times (each with its own reasoning path) and then take a majority vote or best-consensus answer. This was shown to boost accuracy on reasoning tasks, as the ensemble of different reasoning paths tends to cancel out random errors . For instance, DeepSeek observed that after RL training, using a majority vote over several reasoning outputs raised accuracy from 71% to 86.7% on a medical exam task, closing the gap to the top-tier model . This approach, known as Self-Consistency, was originally proposed in 2022 and remains a powerful test-time method: the model basically “rethinks” the problem many times and the most consistent result is chosen as the final answer, reducing occasional reasoning mistakes.

Researchers have also explored more elaborate search-based inference methods. These include using Beam Search or Monte Carlo Tree Search (MCTS) over the space of possible reasoning chains (DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning). For example, one 2024 study guided LLM reasoning with an AlphaZero-like tree search, essentially treating the model as a game player that explores different move sequences (reasoning steps) and uses a value network to pick the best path . Another approach integrated a formal proof assistant’s feedback with MCTS to guide an LLM in solving math proofs . These search-based inference techniques can systematically explore multiple reasoning branches and backtrack when a line of thought appears unpromising. In complex domains like theorem proving or planning, such tree-of-thought methods have shown promise, though they come with higher computational cost and complexity at inference time. So far, none of these search-based methods alone has surpassed the performance of heavily-trained models like OpenAI’s o1 on general reasoning benchmarks , but they continue to be an active research area.

In summary, inference-time scaling methods do not modify the model’s weights – instead, they give the model more opportunities to reason during decoding. Whether by extending the allowed reasoning length, sampling multiple solutions and choosing the best, or systematically searching through thoughts, these techniques can significantly improve outcomes on reasoning tasks without additional training . They are especially useful as a quick way to boost performance of an existing model: if you have a decent base model, just letting it reason more thoroughly (e.g. “let’s think step by step”) and aggregating its answers can yield better accuracy. The trade-off is usually latency and compute at inference – more steps or multiple samples mean slower responses. Effective test-time scaling remains an open challenge, but it is a critical tool in the reasoning toolkit.

2. Pure Reinforcement Learning (RL-Only Training)

Pure reinforcement learning involves training a model to improve its reasoning by learning from trial and error, using a reward signal, without any supervised dataset of correct solutions. In this approach, the model starts from a pretrained base and is optimized via RL on a reward function that captures successful reasoning – for example, solving a puzzle, getting a question correct, or following logical constraints. The recent DeepSeek-R1-Zero is a landmark example demonstrating the potential of pure RL for reasoning: it was trained entirely via reinforcement learning (no supervised fine-tune first) and “numerous powerful and interesting reasoning behaviors naturally emerged” from this process (deepseek-ai/DeepSeek-R1 · Hugging Face). During training, the model discovered strategies like checking its own answers and writing longer scratchpads to maximize its reward, effectively learning to reason better on its own .

The advantage of pure RL is that the model is not constrained by human-written examples – it can, in theory, innovate new reasoning strategies that humans didn’t directly teach it. For instance, in DeepSeek-R1-Zero, the model learned to do self-verification (explicitly verifying intermediate steps) purely because it helped achieve the reward of correct answers . Other researchers in 2024 have similarly reported that RL can induce self-correction behavior in language models. Kumar et al. (2024) describe training language models to self-correct via RL, letting the model iteratively propose and refine answers and rewarding it when it corrects mistakes (DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning). Such approaches treat the reasoning process as a sequential decision-making problem: each step of reasoning influences the final reward, and the model is optimized to produce sequences that yield high reward (e.g., accurate final answers or high logical consistency).

However, pure RL comes with significant challenges. One issue is that, without any supervised guidance, the model might exploit the reward in unintended ways or produce unnatural outputs. As seen with R1-Zero, quality issues like gibberish text, repetitive loops, or mixing languages can occur (deepseek-ai/DeepSeek-R1 · Hugging Face) when the model tries every trick to maximize reward. The RL optimization might encourage correct reasoning but not penalize poor readability or irrelevant verbosity unless those are part of the reward. Another challenge is defining a good reward function for reasoning. If the only reward is “answer is correct at the end,” the model gets very sparse feedback, which makes training difficult. Researchers have addressed this by designing process-based rewards – e.g., giving partial credit for each correct intermediate step (a concept explored by Uesato et al. 2022 and Lightman et al. 2023) . Process supervision via RL can guide the model to reason correctly step-by-step, not just get the final answer, but it requires careful setup of what to reward at each step .

Despite hurdles, the pure RL approach has shown it can produce top-tier reasoning models. DeepSeek-R1-Zero achieved performance close to GPT-4-level on some benchmarks purely through RL optimization, which is a remarkable result (DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning). This suggests a potential future where models could learn to reason with minimal human examples, simply by interacting with problems and receiving feedback. We are also seeing pure RL being used in specific domains: for instance, in code generation, an LLM can be trained via RL to pass unit tests, effectively learning to logically debug its code. In robotics (where an agent needs to reason about actions), RL on language models is being investigated to allow planning through trial-and-error in simulations. Pure RL thus offers a way to discover emergent reasoning skills, but in practice it is often paired with other methods to rein in its excesses. As we saw, DeepSeek ultimately combined RL with some supervised data to get the best of both worlds – which leads us to the next strategy.

3. Supervised Fine-Tuning + Reinforcement Learning (Hybrid RLHF Approaches)

Combining supervised fine-tuning (SFT) with reinforcement learning has become the standard recipe for building aligned and high-performing LLMs, popularized by techniques like Reinforcement Learning from Human Feedback (RLHF). In the context of reasoning models, this hybrid approach means you first teach the model via examples (SFT), then refine it via feedback-driven optimization (RL). The supervised phase might involve feeding the model many step-by-step solutions or high-quality answers so it learns the basics of reasoning and fluency. The RL phase then further optimizes the model’s behavior according to a reward function, often to better align with correctness or human preferences.

OpenAI’s ChatGPT and InstructGPT are classic instances of SFT+RLHF: they first did supervised fine-tuning on demonstrations of ideal answers, and then applied RL using a reward model to align the outputs with what humans prefer (which includes aspects of correctness, helpfulness, etc.). For reasoning tasks, this two-stage approach helps ensure the model is both capable and aligned. The supervised step gives it knowledge of how to reason (since it learns from human solutions), and the RL step can push it to avoid errors and undesirable traits by leveraging feedback signals. Anthropic’s Claude model similarly uses a hybrid approach, where a supervised base is further tuned with a form of RL guided by a “Constitution” of principles (a variant of RLHF without direct human intervention in the loop). These processes have proven effective in practice – for example, models like GPT-4 that underwent extensive SFT and RLHF are among the top performers in both reasoning benchmarks and helpfulness alignment tests (Exploring Reasoning LLMs and Their Real-World Applications).

The DeepSeek-R1 pipeline we discussed exemplifies the power of mixing SFT and RL. Initially, a bit of SFT on reasoning data fixed readability and gave the model a grounding (deepseek-ai/DeepSeek-R1 · Hugging Face), then RL greatly boosted raw reasoning skill , and finally another SFT (with filtered data) plus RL fine-tuned the model to be well-behaved and broadly skilled (DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning). Such multi-turn alternating optimization is essentially SFT+RL on repeat. Each SFT phase can be seen as realigning the model to human-like distribution (so it doesn’t drift too far in its own direction), and each RL phase as pushing the frontier of capability under the guidance of a reward function.

One popular configuration of SFT+RL for reasoning models is: Supervised Fine-Tuning on chain-of-thought demonstrations, followed by RLHF with a reward model that values correct reasoning. Recent research suggests that if you have a way to automatically judge the correctness of an answer (e.g., a programmatic verifier for math problems, or human ratings), using that in RL can dramatically improve performance. For example, OpenAI’s let’s verify step by step approach (Lightman et al. 2023) used a verifier to give feedback on each step of reasoning in math, thereby refining the model’s reasoning via RL on those signals . Another example: a 2023 paper Math-Shepherd trained a reward model to judge the validity of each step in a math proof, and then did RL to encourage the LLM to generate only valid steps . These are instances of SFT+RL where SFT provides initial reasoning ability and RL then enforces correctness rigorously.

From an industry perspective, the SFT + RL approach is attractive because it leverages human expertise and preferences effectively. Supervised fine-tuning leverages existing data (or expert demonstrations), and RLHF allows iterative improvement based on real feedback loops. Companies like OpenAI have leaned heavily on RLHF to align their models with user expectations. In the realm of open-source, techniques like Reinforcement Learning from AI Feedback (RLAIF) use AI critics instead of human annotators to similarly refine models after a supervised stage – again combining an initial SFT model with an RL phase for polishing. The key benefit over pure RL is that the supervised step often makes training more stable and sample-efficient (the model starts closer to a good solution), and the RL step can then focus on more nuanced improvements (like avoiding rare errors or tailoring to user preferences). Indeed, RLHF has been called the “secret sauce” behind ChatGPT and GPT-4’s success (GitHub - AI4Finance-Foundation/FinGPT: FinGPT: Open-Source Financial Large Language Models! Revolutionize We release the trained model on HuggingFace.), underscoring how crucial this hybrid method is for state-of-the-art performance.

4. Pure Supervised Fine-Tuning and Distillation

The fourth strategy is to rely solely on supervised learning signals to build a reasoning model – that is, fine-tuning on curated datasets of prompts and solutions, and optionally using knowledge distillation from a stronger model to guide the training. This approach forgoes any direct reinforcement learning; instead, it tries to encode reasoning behavior through examples and mimicry.

A straightforward version is Supervised Fine-Tuning (SFT) on reasoning data: collect a large set of problems with correct step-by-step solutions (which could be human-written or generated by a capable model), and train the model to reproduce those solutions given the problems. Many open-source chat models and reasoning models have been built this way, because it’s easier to implement than RLHF and doesn’t require designing a reward function or having human feedback for each output. For instance, models like Vicuna, Alpaca, and others were trained by fine-tuning on datasets of question-answer pairs (some of which involve reasoning) that were distilled from larger models. In the reasoning domain, one notable technique is self-instruct or self-generation: use a powerful LLM (like GPT-4) to generate a large set of reasoning examples (questions with detailed CoT solutions), and then fine-tune a smaller model on that synthetic dataset. This is purely supervised (the smaller model just learns to imitate the teacher’s reasoning traces) and has been shown to impart surprising reasoning ability to the smaller model.

Knowledge distillation takes this a step further by having the smaller “student” model not only imitate outputs, but effectively compress the knowledge of a larger “teacher” model. In 2024, multiple studies focused on distilling chain-of-thought reasoning from big models into smaller ones. Feng et al. (2024) describe CoT distillation as a powerful way to transfer reasoning skills – the student model is trained to mimic the important steps in the teacher’s reasoning, rather than every token equally ( Keypoint-based Progressive Chain-of-Thought Distillation for LLMs). By identifying key reasoning milestones (or “keypoints”) in the teacher’s solutions and focusing the training on those, they achieved better learning of reasoning with a smaller model . Another work (Xu et al. 2024) similarly found that carefully distilling the reasoning process yields a student that approaches the teacher’s ability on reasoning tasks . In practice, this means you can take a very large reasoning model (say 70B or 180B parameters) and use its outputs to train a 7B or 13B model that still performs well on complex tasks – a hugely cost-effective outcome.

DeepSeek’s team also validated the effectiveness of pure SFT+distillation in their pipeline: they reported that distilling the reasoning patterns of a large model into a smaller model outperformed training that smaller model with RL from scratch (deepseek-ai/DeepSeek-R1 · Hugging Face). They distilled DeepSeek-R1 (which is 37B active parameters in a larger MoE setup) into a 32B dense model and saw it beat a baseline where the 32B model had been directly RL-fine-tuned . This demonstrates that a well-trained teacher model can impart reasoning skills to a student more effectively than the student might learn on its own via RL. The open-source community has embraced this: as noted, DeepSeek released a whole suite of distilled models (1.5B up to 70B) that were purely fine-tuned on R1’s generated data . These distilled models achieve state-of-the-art results among models of comparable size , showing the viability of the pure supervised approach when it leverages a good teacher.

The pure SFT approach is especially appealing for resource-constrained developers or academics. It sidesteps the complexity of RL training and reward design, and instead uses offline data which can be curated once and reused. Many “open instruction-tuned” models are built this way by harvesting high-quality outputs from ChatGPT/GPT-4 (effectively treating GPT-4 as the reasoning expert to distill from). The drawback is that the model is limited by the quality and scope of the data. If your supervised dataset doesn’t cover certain kinds of reasoning or is too small, the model won’t learn to handle those cases. There’s also a risk of the model overfitting to the style of the data and not being as generally adaptable as an RL-trained model that has explored various possibilities. Nonetheless, given the success of projects like Dolly, Vicuna, and distilled DeepSeek models, it’s clear that with enough diverse and well-crafted examples, pure supervised fine-tuning can produce a strong reasoning model. It’s a simpler pipeline: just “train on lots of reasoning examples,” possibly augmented by distillation from a top-tier model to bootstrap the process (Keypoint-based Progressive Chain-of-Thought Distillation for LLMs). As such, it remains a key strategy, often used in combination with or as a precursor to the other methods.

Industry Applications of Reasoning Models

Reasoning-oriented AI models are being applied across a wide range of industries to tackle tasks that demand complex decision-making and multi-step analysis. Below we highlight how such models are benefitting a few major sectors, with examples drawn from recent applications and official sources:

• Finance: In finance, reasoning LLMs assist with investment analysis, trading strategies, and financial advice. Models like BloombergGPT (a 50B-parameter finance-trained LLM) have shown strong ability in question answering and analysis of financial documents (GitHub - AI4Finance-Foundation/FinGPT: FinGPT: Open-Source Financial Large Language Models! Revolutionize We release the trained model on HuggingFace.). However, BloombergGPT’s training was extremely costly (an estimated $3M over 53 days) , which is why newer approaches like FinGPT focus on lightweight fine-tuning and reasoning to continuously adapt models to fast-changing financial data . FinGPT leverages open-source LLMs and techniques like RLHF to allow personalized financial assistants – for example, adjusting the model to a user’s risk preferences or portfolio context . Reasoning models in finance can interpret long financial reports, perform step-by-step risk assessment, or generate a chain-of-thought explaining a stock recommendation. Such transparency is crucial in finance. Companies are also exploring using these models for automated fraud detection and auditing, where the model must logically go through transactions and flag anomalies. Overall, the ability to think through a complex financial scenario (rather than just retrieve info) makes reasoning LLMs valuable for analysts and advisors.

• Healthcare: The medical field is leveraging reasoning LLMs for diagnostic support, medical Q&A, and summarizing patient interactions. Medical questions often require logical deduction – e.g., combining symptoms and test results to narrow down a diagnosis – which reasoning models can handle by parsing through each piece of evidence. For instance, Google’s Med-PaLM 2 (an LLM fine-tuned for medicine) has demonstrated expert-level reasoning on medical exam questions, including providing step-by-step justifications for its answers. In clinical settings, products like the Nuance Dragon Ambient eXperience (DAX) use AI (powered by models with advanced NLP capabilities) to automatically generate clinical notes from doctor-patient conversations ([

Ambient Clinical Intelligence: Generating Medical Reports with PyTorch | PyTorch

• ](https://pytorch.org/blog/ambient-clinical-intelligence-generating-medical-reports-with-pytorch/#:~:text=This article will showcase how,the technologies that enable it)). While much of that is summarization, a reasoning component helps ensure the notes logically reflect the conversation and medical context. Reasoning models are also being used to power medical chatbots that can triage patients by asking a series of questions (planning the interview dynamically based on previous answers). In healthcare, errors can be life-threatening, so the step-by-step verification that reasoning models provide is highly valued – e.g., an AI doctor assistant can explain its rationale for a treatment recommendation, allowing the human doctor to double-check each step. Early studies in 2025 indicate that such AI assistants, when using reasoning, can achieve more accurate and trustworthy diagnoses compared to simpler models, because they won’t as easily be fooled by superficial cues and can handle multi-factor conditions.

• Robotics and Automation: Robotics has embraced large language models as high-level planners, giving rise to the concept of Large Action Models (LAMs) that integrate reasoning, planning, and execution (Large Action Models (LAMs): Applications and Challenges) . A robot operating in an unstructured environment (like a home or a factory floor) needs to make decisions step-by-step, often based on sensor inputs and goals. Embedding a reasoning LLM in the control loop allows the robot to plan actions in natural language (“First, check if the object is on the table. If not, look in the cupboard, then grasp it with the gripper...”) which the system can then execute. For example, LightPlanner (2025) uses a lightweight LLM to plan household tasks; it employs a hierarchical reasoning process to handle errors (if an action fails, it reasons about why and tries an alternative) (LightPlanner: Unleashing the Reasoning Capabilities of Lightweight Large Language Models in Task Planning) . In robotics, reasoning models also facilitate human-robot interaction – the robot can understand complex instructions from a human by breaking them down. A user might say, “Fetch me the red book from the shelf in the study room after checking if it’s not under the table.” A reasoning-enabled robot can parse this, plan the navigation, the checking action, the fetching action in sequence, and even clarify if some part is ambiguous. Beyond physical robots, in process automation (RPA) and control systems, these models can serve as decision engines that simulate a human operator’s thought process for monitoring systems, diagnosing issues, and planning responses. The use of reasoning LLMs in robotics is still emerging, but early trials show that it greatly improves robustness – the robot can handle unexpected situations by reasoning through them, rather than being stuck when something falls outside of a predefined script .

• Other Sectors: Virtually any domain with complex workflows or decision trees can benefit from reasoning models. In education, they are used as intelligent tutors that can break down solutions step-by-step for students and adjust the teaching strategy if the student is confused (essentially reasoning about the student’s needs). In customer support, reasoning LLMs can operate as multi-turn assistants that figure out what a customer’s underlying issue is through dialog, instead of just answering FAQ-style; they keep track of context and deduce the best solution even if the question is vague. Multi-agent systems also use reasoning LLMs to enable more sophisticated interactions – for example, two agent bots can negotiate or collaborate on a task by exchanging reasoning (in natural language) about the task, which is far more flexible than passing fixed signals (Exploring Reasoning LLMs and Their Real-World Applications). In finance and law, as mentioned, their use is expanding – legal reasoning models can draft and analyze contracts by logically connecting clauses and identifying inconsistencies. Scientific research is another area: tools like ChatGPT have been augmented with reasoning modules to design experiments or analyze experimental results step-by-step, assisting scientists in hypothesis testing. The broad applicability across these sectors underscores that whenever complex logic or multi-step decision making is involved, reasoning AI models are becoming go-to solutions.

Budget Considerations for Building Reasoning Models

Developing and deploying reasoning models comes with varying costs, and effective strategies can differ widely for startups, large enterprises, or resource-constrained teams. Here we outline cost-related considerations and strategies:

• Training Costs – Large-Scale vs. Efficient Training: Training a cutting-edge LLM from scratch is enormously expensive – recent estimates put OpenAI’s GPT-4 training at ~$78 million, and Google’s Gemini Ultra at $191 million in compute (The Cost of Fine Tuning an LLM - Red Marble). Such efforts (and even smaller but still hefty ones like Databricks’ 15B model at $10M) are beyond reach for most organizations . Large enterprises with deep pockets or special data (e.g., Bloomberg in finance) might invest in training a bespoke model, but even they face a cost-benefit question. The good news is that to get reasoning capabilities, one doesn’t necessarily need to train a new base model from scratch. As highlighted earlier, post-training fine-tuning (SFT, RLHF) can yield big reasoning gains at a fraction of the cost of pre-training (DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning). Startups and smaller labs usually opt to fine-tune existing open-source models (like Llama-2, Mistral, etc.) on domain-specific or reasoning data. This can often be done for a few hundred dollars in cloud compute, especially with models under 10B parameters. For instance, one report noted that fine-tuning a 7B model on a specialized dataset can cost on the order of $100 on cloud GPUs (Ask HN: Most efficient way to fine-tune an LLM in 2024?) – a trivial amount compared to training huge models from scratch. So, a cost-effective strategy is: use a pre-trained base model (possibly one released by a big company) and focus budget on fine-tuning it for reasoning. DeepSeek’s approach also exemplified this – they started with an existing base (DeepSeek V3) and only spent compute on the RL/SFT stages, which is far cheaper than creating a 37B MoE model from scratch.

• Open-Source Models and Community Data: Another budget-friendly strategy, especially for startups, is to leverage the rich ecosystem of open-source LLMs and publicly available instruction datasets. Models like Llama 2, Mistral, Falcon, etc., can be downloaded for free, and many have strong general capabilities. By fine-tuning or distilling these models on reasoning data (which could be collected from sources like Stack Exchange explanations, proofs, or via synthetic generation from GPT-4), one can obtain a competent reasoning model without proprietary access. The Open-R1 project, for example, is a community effort to reconstruct DeepSeek-R1’s training pipeline and data openly (Open-R1: a fully open reproduction of DeepSeek-R1 - Hugging Face) – initiatives like this mean that the cutting-edge techniques are reproduced in accessible ways. Additionally, companies like Hugging Face host many fine-tuned reasoning models (some distilled from GPT-4) that one can use directly or as a starting point. This greatly lowers the entry barrier – instead of paying API fees or training costs, a startup can pick an open 13B model that has chain-of-thought ability and run it on a modest GPU. The trade-off might be some performance gap to the absolute state-of-art, but often this gap is small for practical use, as seen with DeepSeek’s own claim of reaching GPT-4 quality at one-tenth the price by leveraging open approaches (R1 is reasoning for the masses - by Charlie Guo).

• Operational Costs (Inference and Deployment): Running a reasoning model in production has its own cost considerations. Reasoning models tend to use more tokens (due to the chain-of-thought) and possibly multiple inference passes (as with self-consistency or tool usage), meaning higher computational cost per query. Large enterprises can afford to deploy big models behind their services (e.g., using clusters of GPUs or specialized hardware), but startups might need to optimize. Techniques like model quantization (running models at 8-bit or 4-bit precision) can drastically cut memory and compute costs for inference, allowing even a 30B model to run on a single high-end GPU or a few CPU cores. There are also parameter-efficient serving strategies: for instance, using a smaller distilled model for most queries and only resorting to a larger model for particularly complex queries (cascaded deployment). Cloud providers offer on-demand GPU inference, so a startup could scale usage with demand rather than maintaining expensive hardware 24/7. Another route is using APIs of large models (OpenAI, etc.) for the hardest tasks and using an in-house model for easier tasks; however, API costs can add up and pose their own budget challenges if usage is high. The key is to balance model size and reasoning depth with the cost envelope. Often, a moderately-sized model (6B-13B parameters) with good training can handle a large fraction of tasks with reasoning if prompted well, at a vastly lower running cost than a 70B+ model.

• Choosing the Right Method for the Budget: Each training method discussed has different cost implications. Inference-time scaling is cheap from a development standpoint (no extra training), but it makes each inference more expensive (e.g., running 10 sampled solutions instead of 1). This might be fine for low-volume, high-stakes queries (like research analyses), but for high-throughput systems, one might prefer to invest more in training to make single-pass inference accurate. Pure RL training can be expensive in terms of the number of trial runs needed – it’s notoriously sample-inefficient, often requiring millions of queries through the model to get significant improvement. This translates to substantial GPU time. Thus, pure RL might be feasible only for well-funded teams or when using smaller models. SFT+RL (RLHF) pipelines also require human or AI feedback generation, which has a cost (either paying human annotators or the compute to run a judge model). OpenAI and others have spent many millions of dollars on RLHF data collection. Startups can mitigate this by using off-the-shelf reward models or by focusing RLHF on narrow domains (reducing the scope of feedback needed). Pure SFT is arguably the most budget-friendly to get started: if you have or can generate a dataset, you can fine-tune an open model in a matter of hours. Distillation adds some overhead (you need a teacher model to generate data, which if it’s an API like GPT-4, could incur usage costs). Some projects deliberately use cheaper teacher models or automated heuristics to generate reasoning data to avoid API fees.

In conclusion, the path you take should align with your resource level:

• A startup or small lab should capitalize on open models, supervised fine-tuning with available data, and maybe light RLHF with open-source reward models. Keep models small-to-medium for manageable inference. Use cloud GPUs only as needed. The FinGPT example is apt: by using open data and models, they claim fine-tuning costs in the hundreds of dollars versus millions for training a new model (GitHub - AI4Finance-Foundation/FinGPT: FinGPT: Open-Source Financial Large Language Models! Revolutionize We release the trained model on HuggingFace.).

• A large enterprise might train a custom reasoning model if the use-case demands (and justify it with proprietary data advantages). But even then, leveraging existing architectures and doing heavy post-training fine-tuning is usually more cost-effective than raw training. Enterprises also consider maintenance cost: a model like BloombergGPT might need retraining or frequent updates, which is expensive, so they are exploring continuous fine-tuning (which reasoning models handle well by incrementally learning new information) .

• In resource-constrained environments (e.g., on-device AI or embedded systems), using distilled models or smaller “reasoning specialists” is key. One might distill a 70B model down to 7B and then quantize it so it can run on a mobile device or a single CPU – sacrificing some accuracy but achieving autonomy from cloud resources. The open research community’s focus on distillation (deepseek-ai/DeepSeek-R1 · Hugging Face) and efficient fine-tuning is directly enabling this: we now have models like Llama-2 7B that, with the right fine-tuning, can perform complex reasoning surprisingly well, all while being deployable in low-resource settings.

Ultimately, cost-effective reasoning AI is about leveraging what’s already available and tailoring it rather than reinventing the wheel. With the plethora of 2024–2025 research and open resources, even smaller players can build sophisticated reasoning models by smartly combining techniques – using public models, community data, and focusing compute on the critical fine-tuning steps. This democratization of reasoning models means we’ll continue to see broad adoption across industries without each player needing an enormous budget. The literature and industry trends agree: the gap between cutting-edge capability and accessible AI is closing, thanks in large part to these refined methods of building reasoning models (R1 is reasoning for the masses - by Charlie Guo) .

Sources: Recent research papers and industry reports were referenced inline, including DeepSeek-R1: Incentivizing Reasoning Capability in LLMs (2024/25) (DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning), discussions of OpenAI’s and Anthropic’s models (Exploring Reasoning LLMs and Their Real-World Applications), and insights from community projects like FinGPT (GitHub - AI4Finance-Foundation/FinGPT: FinGPT: Open-Source Financial Large Language Models! Revolutionize We release the trained model on HuggingFace.). Industry use cases were informed by official blogs and publications (e.g., PyTorch healthcare case study ([

  Ambient Clinical Intelligence: Generating Medical Reports with PyTorch | PyTorch

](https://pytorch.org/blog/ambient-clinical-intelligence-generating-medical-reports-with-pytorch/#:~:text=This article will showcase how,the technologies that enable it)), analytics on Large Action Models in robotics (Large Action Models (LAMs): Applications and Challenges)). These illustrate the state-of-the-art approaches and practical considerations as of 2024–2025 in building reasoning AI systems.