Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

Reinforcement learning can make a model much more likely to produce a correct answer on its first attempt without expanding the set of problems it can solve. This study tests that distinction by comparing base models with their RL-trained counterparts across increasing numbers of sampled answers.

Its central metric is pass@k: a problem counts as solved if at least one of the sampled candidates is correct. In the evaluated settings, RL-trained models often lead at small sampling budgets, while base models catch up or overtake them at larger budgets. The authors also compare the actual problems solved and examine the probability of reasoning paths, rather than relying only on the aggregate curves.

That evidence supports a narrower conclusion than the slogan that RL cannot teach reasoning. The study covers particular public models, training recipes and evaluators. It explicitly leaves room for different exploration strategies, richer feedback and agent-environment interaction. It also finds a different pattern for distillation, where a teacher supplies information beyond the student's own sampled outputs.

For an engineer, the practical question is whether adaptation improves the service at its real operating budget. A correct candidate buried among many wrong ones is useful only if your system can identify it before the deadline.

A customer may request reinforcement learning after seeing an impressive reasoning score. Before proposing a training job, determine whether the current model already produces correct solutions occasionally, whether a reliable verifier exists and how much inference budget the workflow can afford.

For code generation, a test runner may help select among candidates, although incomplete tests can accept incorrect programs. For policy advice, there may be no comparably trustworthy automatic check. The same pass@k improvement therefore has different operational value in the two products.

Use this paper to design a comparison between adaptation and inference-time search. Include latency, total generated tokens and selected-answer correctness. Keep task coverage visible too: a tuning change that benefits common requests may damage a rare but important customer workflow. Knowledge distillation explains a separate option when a stronger teacher can supply useful demonstrations.