mlsanityData to 14 Oct 2026

UniOPSD: Unifying Outcome and Hindsight Feedback for Agentic Reinforcement Learning

arXiv 2609.34810 ↗First posted 28 Sep 2026cs.AIcited by 0

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

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Abstract (arXiv)

Reinforcement learning has become an effective approach to training language model agents, but sparse and delayed outcome rewards provide limited guidance for credit assignment across long interaction sequences. Recent work on on-policy self-distillation (OPSD) offers complementary supervision by evaluating a policy's sampled responses under privileged training-time context. However, our diagnostics show that positive average agreement between outcome and hindsight feedback coexists with substantial local disagreement, raising the question of how to allocate influence between them at each decision. We introduce UniOPSD (Unified On-Policy Self-Distillation), which unifies these feedback sources through adaptive local credit arbitration. UniOPSD constructs comparable credit estimates from environmental returns and successful-peer hindsight at shared interaction anchors. Historical agreement determines the global mixing level, while current signal availability and relative precision adjust each source's influence at individual decisions. The episode-level outcome contribution is retained, and bounded token modulation refines the fused step credit for policy optimization. With Qwen2.5-3B-Instruct and Qwen2.5-7B-Instruct, UniOPSD achieves ALFWorld success rates of 82.8% and 83.6%, WebShop success rates of 75.0% and 82.0%, and Search-QA aggregate accuracies of 45.3% and 49.8%, respectively. On 3B WebShop, UniOPSD improves over SDAR by 7.0 percentage points. Our code is available at https://github.com/Zenghuang-Fu/Uniopsd

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 34 of its 37 references that are in the map. First posted on arXiv 28 Sep 2026.

Builds on

34 of 37 references.

Closest ancestors · 11

Same direction, newest first

  1. 2026AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning6 Aug
  2. 2026Agentic Reinforcement Learning with Self-Distilled Reward Shaping4 Aug
  3. 2026Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning26 May
  4. 2026Self-Distilled Agentic Reinforcement Learning14 May
  5. 2026Resolving Action Bottleneck: Agentic Reinforcement Learning Informed by Token-Level Energy14 May
  6. 2026PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learning10 May
  7. 2026Skill-SD: Skill-Conditioned Self-Distillation for Multi-turn LLM Agents12 Apr
  8. 2026Hindsight Credit Assignment for Long-Horizon LLM Agents7 Mar
  9. 2025Group-in-Group Policy Optimization for LLM Agent Training16 May
  10. 2023Voyager: An Open-Ended Embodied Agent with Large Language Models25 May
  11. 2023Reflexion: Language Agents with Verbal Reinforcement Learning20 Mar

Other references · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  5. 2024Qwen2.5 Technical Reportcited by 4,942
  6. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  7. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  8. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  9. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,251
  10. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,205
  11. 2023On-Policy Distillation of Language Models: Learning from Self-Generated Mistakescited by 939
  12. 2021Conflict-Averse Gradient Descent for Multi-task Learningcited by 459
11 more
  1. 2026Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Modelscited by 455
  2. 2026Self-Distilled RLVRcited by 131
  3. 2019Hindsight Credit Assignmentcited by 79
  4. 2026TCOD: Exploring Temporal Curriculum in On-Policy Distillation for Multi-turn Autonomous Agentscited by 47
  5. 2026StepOPSD: Step-Aware Online Preference Self-Distillation for Agent Reinforcement Learningcited by 16
  6. 2026Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularizationcited by 12
  7. 2026On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversitycited by 10
  8. 2026Self-Distilled Policy Gradientcited by 8
  9. 2026Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remembercited by 7
  10. 2026The Role of Feedback Alignment in Self-Distillationcited by 4
  11. 2026CoEvoKG: Co-Evolving Knowledge Graphs with Self-Evolving Search Agentscited by 3

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026PR-OPD: Privileged Representation On-policy Self-Distillation for Agentic Reinforcement Learning
  2. 2026OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning
  3. 2026SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
  4. 2026Environmental Feedback Modeling Matters: Rethinking Feedback Treatment in Agentic Hindsight Self-Distillation
  5. 2026Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
  6. 2026HERO: Hindsight-Enhanced Reflection from Environment Observations for Agentic Self-Distillation
  7. 2026Localizing Credit at the Divergence: Path-Conditioned Self-Distillation for LLM Reasoning
  8. 2026From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models