mlsanityData to 14 Oct 2026

Gated-BEPO: Confidence-Gated Bellman Credit Assignment for Large Language Model Agents

arXiv 2608.06861 ↗First posted 7 Aug 2026cs.AIcited by 2

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

Abstract (arXiv)

Training large language model agents in long-horizon environments requires assigning credit from sparse terminal outcomes to individual actions. Existing critic-free methods propagate trajectory-level rewards uniformly across steps, while recent approaches construct step-level groups by matching repeated states and compare actions within each group. The former cannot distinguish useful actions in failed trajectories from ineffective actions in successful ones. The latter rely on step credit derived directly from individual trajectory outcomes and fixed-weight fusion with episode-level credit. We propose Gated-BEPO, which derives step-level credit from empirical rollout graphs. For each rollout group, Gated-BEPO constructs an empirical graph and estimates node values through a mean-backup Bellman fixed point that reflects the empirical action distribution of the current policy. We then accumulate these temporal-difference residuals along each sampled trajectory using generalized advantage estimation, yielding step-level Bellman advantages that capture both immediate and downstream effects. To adaptively fuse episode- and step-level credit, a confidence gate incorporates Bellman credit only at states with multiple observed successors and otherwise uses episode-level credit. Experiments on WebShop, ALFWorld, and visual Sokoban show consistent improvements across language and vision-language models, while diagnostic ablations support the effectiveness of Bellman fixed-point value estimation and show that step-level credit should be incorporated selectively rather than uniformly into the final advantage.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 28 of its 38 references that are in the map. First posted on arXiv 7 Aug 2026.

Builds on

28 of 38 references.

Closest ancestors · 8

Same direction, newest first

  1. 2026Beyond Trajectory-Level Attribution: Graph-Based Credit Assignment for Agentic Reinforcement Learning26 May
  2. 2026GAGPO: Generalized Advantage Grouped Policy Optimization13 May
  3. 2026Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks26 Feb
  4. 2025Agent Lightning: Train ANY AI Agents with Reinforcement Learning5 Aug
  5. 2025Reinforcing Multi-Turn Reasoning in LLM Agents via Fine-Grained Reward Structure and Credit Assignment17 May
  6. 2025Group-in-Group Policy Optimization for LLM Agent Training16 May
  7. 2025Reinforcement Learning for Long-Horizon Interactive LLM Agents3 Feb
  8. 2023Reflexion: Language Agents with Verbal Reinforcement Learning20 Mar

Other references · 20

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  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. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  7. 2017Deep reinforcement learning from human preferencescited by 3,912
  8. 2023Let's Verify Step by Stepcited by 3,789
  9. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  10. 2020Learning to summarize from human feedbackcited by 2,588
  11. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  12. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
8 more
  1. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,251
  2. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,205
  3. 2024WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learningcited by 256
  4. 2024VinePPO: Refining Credit Assignment in RL Training of LLMscited by 183
  5. 2018RUDDER: Return Decomposition for Delayed Rewardscited by 173
  6. 2025WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learningcited by 165
  7. 2019Hindsight Credit Assignmentcited by 79
  8. 2024Large Language Models Can Self-Improve At Web Agent Taskscited by 55

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026PRICE the Action Chunks: Physical Relational Credit Assignment for Embodied Reinforcement Learningcited by 0
  2. 2026VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Granularity-Adaptive Credit Assignment for Long-Horizon LLM Agent Reinforcement Learning
  2. 2026Matched-Context Group Policy Optimization for LLM Agent Training
  3. 2026From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models
  4. 2026RELACE: retrospective likelihood-based action credit estimation for long-horizon language agents
  5. 2026CAPO: Critic-Guided Action-Aligned Policy Optimization for Advancing LLM Agent Capabilities
  6. 2026SAT: Sequential Agent Tuning for Coordinator Free Plug and Play Multi-LLM Training with Monotonic Improvement Guarantees
  7. 2026Verified Critical Step Optimization for LLM Agents
  8. 2026BRACE: Anchored Bellman-Residual Correction for Stale Critics in Asynchronous RL