Why RL Improves Reasoning After SFT
burny_tech · x · 2026-07-12
This post shares insights from a paper explaining why Reinforcement Learning (RL) improves reasoning after Supervised Fine-Tuning (SFT):
- SFT typically provides the full problem-solving process at once, blending the genuinely useful parts into a tangled whole that the model learns as an inseparable block.
- RL, guided by rewards, helps the model break down this information into more reusable skills and routing rules.
- As a result, the model becomes capable of recombining these skills to tackle novel problems it never encountered during SFT.
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