Fine-tuning a reasoning model like DeepSeek R1 typically follows the same two-stage recipe its own developers used: a supervised fine-tuning cold start on a modest set of high-quality chain-of-thought examples to establish the habit of reasoning step by step before answering, followed by reinforcement learning, such as GRPO, against a verifiable reward function on tasks where correctness can be checked automatically, like math problems with a known answer or code that passes or fails real tests. For most enterprises, the more practical path is distillation rather than training a reasoning model from scratch, which means generating chain-of-thought training examples from an existing strong reasoning model and using them to supervised fine-tune a smaller model to imitate that reasoning style on your specific task domain. Keeping the reasoning traces in the training data, rather than only the final answer, matters, since the whole point of these models is generating and using intermediate reasoning rather than jumping straight to a conclusion. Evaluation should specifically check whether reasoning steps are logically consistent, not just whether the final answer happens to be correct. Nanobase AI evaluates whether a client's task has the kind of verifiable reward signal that makes full reinforcement learning worthwhile versus simpler distillation from an existing reasoning model.
Three paths, and most enterprises want the cheapest one
There are three distinct ways to get reasoning-model-style behavior into a deployed model, and they differ enormously in cost and complexity: training a reasoning model from scratch using the full supervised cold-start plus reinforcement learning recipe, distilling chain-of-thought behavior from an already-strong reasoning model into a smaller target model through supervised fine-tuning, or simply prompting a strong general model to reason step by step without any additional training at all. For the overwhelming majority of enterprise use cases, distillation is the right choice: it captures most of the practical benefit of reasoning behavior at a small fraction of the cost and complexity of training reasoning capability from scratch through reinforcement learning.
Comparing the three approaches
The relative cost column is the deciding factor for almost every enterprise case: distillation delivers most of the benefit of training from scratch at a fraction of the infrastructure investment.
| Approach | What it requires | Relative cost | Best fit |
|---|---|---|---|
| Train from scratch (SFT cold-start + GRPO) | Verifiable reward function, RL infrastructure, significant compute | Highest | Frontier model development, novel reasoning domains |
| Distillation | Chain-of-thought examples generated by an existing strong reasoning model, standard SFT pipeline | Moderate | Most enterprise tasks needing reasoning-style output on a smaller, cheaper deployed model |
| Prompting only | A capable general model, no training at all | Lowest | Low-volume use cases, or early validation before investing in distillation |
A concrete distillation workflow
Filtering out traces that reach the wrong final answer, not just formatting the ones that survive, is what keeps distilled reasoning from teaching bad habits.
- Select a strong existing reasoning model to generate training examples from, and have it produce full chain-of-thought reasoning traces plus final answers for a representative sample of your task's prompts.
- Filter generated traces for correctness where verifiable (math, code, structured tasks with a checkable answer), discarding examples where the reasoning trace led to a wrong final answer, since training on incorrect reasoning teaches the wrong lesson.
- Format the training data to preserve the full reasoning trace before the final answer, not just the answer alone, since the point of distillation is teaching the target model to reproduce the reasoning process, not just memorize final answers to similar questions.
- Fine-tune the smaller target model using standard supervised fine-tuning (LoRA or full fine-tuning depending on target model size) on these traces, following the same data quality practices as any other supervised fine-tuning project.
- Evaluate specifically on held-out problems requiring genuine reasoning rather than pattern matching to the training set, checking both final answer correctness and whether the reasoning trace itself remains coherent and non-degenerate.
The original DeepSeek recipe, for context
DeepSeek's own approach to training R1 combined a supervised fine-tuning cold start on a modest set of high-quality chain-of-thought examples, establishing the habit of stepwise reasoning before answering, followed by GRPO-based reinforcement learning against verifiable rewards on tasks with checkable correctness, such as math problems with known answers and code that passes or fails real tests. This two-stage approach is genuinely more powerful than distillation for pushing reasoning capability beyond what any existing model can already do, but it also requires the full reinforcement learning infrastructure, reward function design, and compute budget that most enterprise teams have no practical reason to build for a narrower, already-solved reasoning task.
Evaluating reasoning models correctly
A reasoning-tuned model needs an evaluation approach that checks the final answer's correctness but should also sanity-check that the reasoning trace itself is coherent, since it is possible for a model to arrive at a correct final answer through a nonsensical or unfaithful reasoning path, particularly after aggressive fine-tuning. This connects to the general principles in evaluating a fine-tuned model against the base model, with the added dimension that reasoning traces themselves, not just final outputs, are worth spot-checking during evaluation.
Frequently asked questions
Is distillation from a reasoning model legally and technically straightforward?
Technically it is a standard supervised fine-tuning process once you have generated chain-of-thought training examples; check the license terms of whichever model you generate training data from, since some providers restrict using their outputs to train competing models, while open-weight reasoning models are generally permissive for this use.
Does a distilled model reason as well as the original model it was distilled from?
Usually not quite as well on the hardest problems, since distillation transfers a pattern of reasoning behavior rather than the full underlying capability of the larger source model, but it often captures enough of the benefit for well-defined enterprise tasks at a much lower deployment cost.
Do we need reinforcement learning at all for enterprise reasoning tasks?
Rarely. Distillation from an existing strong reasoning model covers most enterprise needs; GRPO or other reinforcement learning approaches are worth the added complexity mainly when the task requires reasoning capability beyond what any existing model can already demonstrate through its outputs.
How Nanobase AI helps
Nanobase AI builds chain-of-thought distillation pipelines that bring reasoning-model behavior into smaller, cheaper-to-deploy models, including correctness filtering and reasoning-trace evaluation, on GPU infrastructure sized for the target deployment rather than the larger source model. This is part of our broader fine-tuning and model optimization work, developed with the backing of our NVIDIA Inception Program membership.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.