Why does LLM RL work despite information-theoretic inefficiency? A deep dive
tokenbender · x · 2026-08-14
The article explores why LLM reinforcement learning (RL) works despite appearing information-theoretically inefficient. Pretraining computes loss on every token, while RL gives a single reward per long rollout, seemingly inefficient. Yet empirically RL works well, and the article speculates on this paradox.
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