A RL lecture revisits how KL regularization changes as methods evolve
cwolferesearch · x · 2026-07-29
- Nat Lambert’s lecture focuses nominally on regularization in RL, but the core topic is how the KL penalty’s role is changing as RL methods evolve.
- The talk also surveys several RL papers that explain why RL can help models generalize better than SFT, with theory backing that claim.
- The thread argues these are “seasonal” ML problems: as one reward-model failure mode gets solved, a structurally similar one tends to reappear elsewhere.
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