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About this paper
Lang Feng, Zhenghai Xue, Tingcong Liu, Bo An
GiGPO addresses a credit-assignment problem in long-horizon language-model agent training by combining trajectory-level and shared-state action comparisons. It groups complete attempts for overall returns, then compares actions taken from repeated states using discounted returns. In tests, it raises success over GRPO in ALFWorld and WebShop, but its step-level signal falls back to GRPO when states do not recur.
Key insights
- Effective multi-turn LLM agent training requires a hierarchical approach to credit assignment, combining both episode-level and fine-grained step-level feedback.
- Recurring environment states across trajectories can be leveraged to efficiently construct step-level action groups for micro-level advantage estimation, minimizing computational cost.
- The core benefits of group-based reinforcement learning (critic-free, low-memory, stable convergence) can be maintained while enabling precise policy refinement in complex, long-horizon tasks.
Summary by alphaXiv.
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