Group Entropy-Controlled Policy Optimization
Entropy control has become an effective tool in reinforcement learning (RL) of large language models (LLMs), helping balance exploration-exploitation trade-off during alignment process. Such RL paradigm is often conducted on mixtures of heterogeneous tasks, which induce distinct entropy regimes under the same policy, making global or token-level entropy regulation insufficient to corresponding heterogeneous needs of exploration. This heterogeneity further makes GRPO-style normalized advantages i
Record details
Published: 17 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Regulation · Safety & alignment
Retrieved: 21 July 2026
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How to cite this record
ethics.ai (17 July 2026), “Group Entropy-Controlled Policy Optimization,” evidence record 11914, https://ethics.ai/record/11914 (originally published by HuggingFace Daily Papers).
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