New Solution for RLVR Entropy Collapse
机器之心 · wechat · 2026-07-15
Reinforcement Learning from Verifiable Rewards (RLVR) has become a vital track in LLM post-training, but it frequently suffers from rapid policy entropy drops, output homogenization, and shrinking exploration spaces. Using an ACL 2026 Outstanding Paper as an example, the article systematically analyzes the token-level mechanism of entropy collapse: whether a token is clipped during an update, the direction of the advantage signal, the original generation probability, and conditional entropy collectively determine if local entropy rises or falls.
The author reinterprets why existing entropy intervention methods are effective yet limited: for instance, DAPO/Clip-Higher opens up "rare but correct" branches, while Positive-Reweighting reduces the over-reinforcement of familiar correct paths. However, these methods mostly offer coarse adjustments to specific tokens without directly controlling the entropy change of each one.
The paper further introduces STEER (Stabilizing Token-level Entropy-changE via Reweighting), which dynamically down-weights tokens based on their expected magnitude of entropy change, acting as a "speed limiter" rather than simply forcing entropy higher. Experiments show that STEER delivers consistent improvements in math reasoning and coding tasks, with replicable gains across different model families and RLVR algorithms.
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