Zhejiang University and Tencent’s STEER targets entropy collapse in RL training
jiqizhixin · x · 2026-07-24
Zhejiang University and Tencent propose STEER to fix entropy collapse in RL for LLMs
Researchers from Zhejiang University and Tencent introduce STEER, a method aimed at a common failure mode in reasoning training: entropy collapse.
Instead of using a heuristic to blindly adjust entropy, STEER estimates how entropy changes and then reweights tokens adaptively. The authors claim this targets a key flaw in RL for LLMs more directly than prior interventions.
According to the post, the method outperforms state-of-the-art baselines on:
- 6 math-reasoning benchmarks
- 3 coding benchmarks
The paper is titled Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective and the code is open-sourced.
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