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This paper proposes Group Entropy-Controlled Policy Optimization (GEPO), a lightweight extension to GRPO that uses group entropy to perform entropy-conditioned asymmetric advantage shaping, addressing heterogeneous entropy regimes across tasks during RL-based alignment of LLMs. Experiments show consistent improvements over GRPO and recent entropy-controlled methods across multiple benchmarks.
This paper introduces Entrocraft, a rejection-sampling method for RL that controls entropy schedules to prevent performance saturation in LLMs. It demonstrates improved generalization and training longevity, allowing smaller models to outperform larger baselines.