aiqbench analysis
SHARPO introduces a novel credit-assignment mechanism for agentic reinforcement learning, addressing the challenge of localized errors in long-horizon tasks by refining Group Relative Policy Optimization at the segment level. By leveraging teacher-student log-probability gaps within environment-facing segments, this method enables more granular reward signals that improve performance on benchmarks like ALFWorld and WebShop without requiring trajectory-level rewards. The approach demonstrates significant gains over existing baselines using Qwen2.5-7B-Instruct, highlighting its potential to enhance the reliability of autonomous agents in complex multi-step interactions.
SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning
arXiv:2610.00838v1 Announce Type: cross 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.00838 [cs.LG]
(or arXiv:2610.00838v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.00838 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 229 words
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