Why RL Matters: From Reinforcement Learning to (Soft) Distillation

The article argues that reinforcement learning is crucial in post‑training because it refines and localizes chain‑of‑thought patterns learned during supervised fine‑tuning, improves model controllability, and can be complemented or substituted by distillation—especially soft distillation—to transfer high‑quality patterns from stronger teachers to weaker models.

LLMchain-of-thoughtdistillation

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Why RL Matters: From Reinforcement Learning to (Soft) Distillation