mlsanityData to 7 Oct 2026

SHARPO: Segment-Level Credit Assignment for Agentic Reinforcement Learning

arXiv 2610.00838 ↗First posted 30 Sep 2026cs.LG cs.AIcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 24 of its 25 references that are in the map. First posted on arXiv 30 Sep 2026.

Builds on

24 of 25 references.

Closest ancestors · 6

Same direction, newest first

  1. 2026AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning6 Aug
  2. 2026TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning30 Jun
  3. 2026Self-Distilled Agentic Reinforcement Learning14 May
  4. 2025Group-in-Group Policy Optimization for LLM Agent Training16 May
  5. 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning24 Apr
  6. 2024ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL29 Feb

Other references · 18

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  4. 2024Qwen2.5 Technical Reportcited by 4,713
  5. 2023Let's Verify Step by Stepcited by 3,598
  6. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  7. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
  8. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  9. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
  10. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 895
  11. 2023On-Policy Distillation of Language Models: Learning from Self-Generated Mistakescited by 840
  12. 2026Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Modelscited by 386
6 more
  1. 2024VinePPO: Refining Credit Assignment in RL Training of LLMscited by 168
  2. 2022Learning by Distilling Contextcited by 128
  3. 2026Self-Distilled RLVRcited by 109
  4. 2025Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Modelscited by 75
  5. 2026StepOPSD: Step-Aware Online Preference Self-Distillation for Agent Reinforcement Learningcited by 14
  6. 2026TREK: Distill to Explore, Reinforce to Refinecited by 1

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  1. 2026Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning
  2. 2026Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning
  3. 2026APPO: Agentic Procedural Policy Optimization
  4. 2026Who Deserves the Reward? SHARP: Shapley Credit-based Optimization for Multi-Agent System
  5. 2026SLCA-GRPO: Resolving Cross-Segment Credit Misattribution in Tool-Calling RL
  6. 2026From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models
  7. 2026AdaStep: Adaptive Step Credit Weighting for Agentic Reinforcement Learning
  8. 2026GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation