arXiv AI October 15, 2026 By Zhengqing Yan, Fan Guo, Xinyang Liu, Yi Zhang, Yao Liu, Qi Liu, Jihao Huang, Kang Song

GDEPO: Group Dual-dynamic and Equal-right Advantage Policy Optimization for Sample-Efficient Reinforcement Learning

The paper introduces GDEPO, a reinforcement learning framework designed to improve sample efficiency in automated theorem proving. GDEPO incorporates dynamic additional sampling, an equal‑right advantage function that separates correctness from reward magnitude, and dynamic additional iterations to refine learning from initially failed samples. Experiments on MinF2F‑test, MathOlympiadBench, and PutnamBench demonstrate that these mechanisms synergistically enhance data utilization and optimization efficiency.

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