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ERR+: Sequential Entropy Resolution for Efficient and Decisive LLM Reasoning
Xrk Arul, Author 2, Author 3, Author 4, Author 5
reasoningreinforcement learningentropymodel optimization
2608.28771
Builder Relevance
1h ago80%
Abstract
The paper presents ERR+, a two-phase RLVR framework that optimizes reasoning quality in large language models by rewarding entropy resolution during the reasoning process.
Reality Card
Core Claim
ERR+ improves both accuracy and response conciseness in large reasoning models by optimizing the internal reasoning structure through a novel reward mechanism.
Method / Result
Experiments demonstrate consistent improvements across five datasets.
Limitations
The joint optimization of the two objectives may induce gradient conflict in early training, which could affect reproducibility.
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