mlsanityData to 14 Oct 2026

VH-GPO: Verified Handoff for LLM Agent Reinforcement Learning

arXiv 2610.14000 ↗First posted 27 Sep 2026cs.LG cs.AIcited by 0

On the map: LLM reasoning → Reinforcement learning for reasoning

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Abstract (arXiv)

Group-relative reinforcement learning methods such as GRPO train large language model (LLM) agents by comparing rollouts for the same task. When every trajectory in a group fails with the same reward, relative advantages collapse toward zero, leaving no useful learning signal. A frozen teacher can recover from a student-visited state, but letting it complete the task makes the recovered suffix entirely teacher-generated. We ask: after repairing a student failure, when should control return to the student? We propose outcome-verified teacher-student handoff, instantiated in Verified Handoff for Group Policy Optimization (VH-GPO). The teacher provides enough support to reach a state from which the student can succeed, then returns control so that successful experience is again student-generated. Given an environment-verified teacher recovery, VH-GPO selects the earliest independently confirmed handoff point found within a bounded search budget. With Qwen2.5-1.5B, VH-GPO increases success rates over GRPO from 59.38% to 94.79% on ALFWorld and from 71.62% to 77.34% on WebShop. Improvements also hold with the 7B student, for which additional teacher and student output tokens amount to 1.77% and 2.61% of regular student rollout output tokens on ALFWorld and WebShop, respectively. Ablation studies demonstrate the effectiveness of the key components. These results support using verified teacher assistance to repair failures while reinforcing the student's ability to complete tasks independently.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 45 of its 50 references that are in the map. First posted on arXiv 27 Sep 2026.

Builds on

45 of 50 references.

Closest ancestors · 12

Same direction, newest first

  1. 2026Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks26 Feb
  2. 2025Group-in-Group Policy Optimization for LLM Agent Training16 May
  3. 2025Understanding R1-Zero-Like Training: A Critical Perspective26 Mar
  4. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan
  6. 2025REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization4 Jan
  7. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search6 Jun
  8. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations14 Dec
  9. 2023Let's Verify Step by Step31 May
  10. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Model29 May
  11. 2023RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment13 Apr
  12. 2017Proximal Policy Optimization Algorithms20 Jul

Other references · 33

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  5. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  6. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  7. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,251
  8. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,205
  9. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 940
  10. 2023On-Policy Distillation of Language Models: Learning from Self-Generated Mistakescited by 939
  11. 2025Group Sequence Policy Optimizationcited by 835
  12. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 737
21 more
  1. 2023Reinforced Self-Training (ReST) for Language Modelingcited by 500
  2. 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learningcited by 386
  3. 2023Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Modelscited by 349
  4. 2018HG-DAgger: Interactive Imitation Learning with Human Expertscited by 266
  5. 2020First return, then explorecited by 266
  6. 2024WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learningcited by 256
  7. 2024Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learningcited by 241
  8. 2024ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RLcited by 228
  9. 2024DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learningcited by 214
  10. 2024Agent Q: Advanced Reasoning and Learning for Autonomous AI Agentscited by 208
  11. 2024VinePPO: Refining Credit Assignment in RL Training of LLMscited by 183
  12. 2018Self-Imitation Learningcited by 138
  13. 2022Jump-Start Reinforcement Learningcited by 112
  14. 2025Agent Lightning: Train ANY AI Agents with Reinforcement Learningcited by 109
  15. 2021ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learningcited by 108
  16. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 84
  17. 2023ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agentcited by 81
  18. 2026Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learningcited by 31
  19. 2026Learning Agentic Policy from Action Guidancecited by 4
  20. 2026DASH-OPD: Discrepancy-Aware Switching with Hysteresis for On-Policy Distillationcited by 3
  21. 2026From Scoring to Acting: Outcome-Verified Comparative Self-Distillation for LLM Agentscited by 2

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  2. 2026Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation
  3. 2026How Off-Policy Can GRPO Be? Mu-GRPO for Efficient LLM Reinforcement Learning
  4. 2026iGRPO: Self-Feedback-Driven LLM Reasoning
  5. 2026Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective
  6. 2025CoRPO: Adding a Correctness Bias to GRPO Improves Generalization
  7. 2025Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training
  8. 2026Where to Start, How to Move: Analyzing Curricula under GRPO for Long-Horizon LLM Agents