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arXiv · 2610.00838

SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning

Xinchen Du·Zhengze Zhou·Wenhui Zhu·Han Yu·Sen Na·Rohit Jain·Alborz Geramifard
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Abstract

Agentic reinforcement learning (RL) trains a large language model (LLM) to act over long, multi-step interactions. However, a single localized error can cause task failure, while trajectory-level rewards provide limited guidance for assigning credit to individual decisions. To address this limitation, we introduce Segment-level Hindsight Advantage Reweighting for Policy Optimization (SHARPO), a credit-assignment mechanism that refines Group Relative Policy Optimization (GRPO) at the level of environment-facing segments. Inspired by the existing on-policy self-distillation (OPSD) method, SHARPO computes teacher-student log-probability gaps within each segment and uses the resulting signal to compute a bounded multiplier on the GRPO advantage. This multiplier is shared by all tokens within the segment, allowing credit to vary across different segments. With Qwen2.5-7B-Instruct, SHARPO outperforms existing baselines on the ALFWorld and WebShop benchmarks, including GRPO, SDAR, RLSD, and StepOPSD.

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Xinchen Du, Zhengze Zhou, Wenhui Zhu, Han Yu, Sen Na, Rohit Jain, Alborz Geramifard. 2026-09-30. SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning. https://arxiv.org/abs/2610.00838

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