Addressing Performance Saturation for LLM RL via Precise Entropy Curve Control
Reinforcement learning (RL) has enabled complex reasoning abilities in large language models (LLMs). However, most RL algorithms suffer from performance saturation, preventing continued gains as RL training scales. This problem can be characterized by the collapse of entropy, a key diagnostic for exploration in RL. Existing attempts focus on preventing entropy collapse through regularization or clipping. However, their resulting entropy curves often exhibit instability in the long term, which hinders performance gains. In this paper, we introduce Entrocraft, a simple rejection-sampling approach that realizes user-customized entropy schedule by biasing the advantage distributions. Entrocra...
| Authors | Bolian Li, Yifan Wang, Yi Ding et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 April 2026 |
| Last updated at source | 10 May 2026 |
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