Why LLM RL Works: Exploring Low-Bias Methods Despite Information-Theoretic Inefficiency
agarwl_ · x · 2026-08-14
The post recommends a deep dive into the mechanics of reinforcement learning (RL) for LLMs. While traditional arguments suggest RL is informationally inefficient compared to pretraining, the author argues that current successful LLM RL methods are essentially low bias.
- Limitations of Value Functions: While they trade off variance with bias, they haven't shown huge gains in LLMs yet.
- Catastrophic Mismatches: Small biases from trainer-inference mismatches often prove catastrophic when scaling up RL runs.
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