Fixing GRPO's credit assignment problem without evaluating every step

Automated excerpt

Abstract:Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. We introduce ProVer, a framework that targets potentially pivotal decisions for fine-grained credit assignment in agentic reinforcement learning. Further analyses demonstrate that informed segment selection improves policy training with modest additional generation overhead, even without a frontier-scale judge model, highlighting the effectiveness and efficiency of selectively targeting pivotal decisions for fine-grained credit assignment in agentic reinforcement learning.

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