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.
More from Research
- HarnessMM cuts agent tokens per task by up to 88% while raising success rates — omarsar0 · 2026-10-26
- Ben Recht: The world needs a truly open language model, and it's easier to build than you think — beenwrekt · 2026-10-26
- New Blog Breaks Black-Box Distillation Into Five Practical Steps — chrisrohlf · 2026-10-26
- DetectorScope turns black-box AI text detectors into auditable ones with sparse autoencoders — _akhaliq · 2026-10-26
- OPST paper shows LLMs self-improve with zero human data, guided only by topic descriptions — _akhaliq · 2026-10-26
- Tencent's HiSTEVE 1.0: hierarchical VLA agent plays Minecraft with native keyboard and mouse, hits 85% SOTA — _akhaliq · 2026-10-26