mlsanityData to 14 Oct 2026

Dual-Axis Policy Optimization for LLM Agents: Bayesian Feedback Attribution and Trajectory Mass Normalization

arXiv 2609.19830 ↗First posted 17 Sep 2026cs.AI stat.MLcited by 1

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

Abstract (arXiv)

Reinforcement learning for LLM agents involves two distinct optimization di- mensions: how environment feedback is exploited within a trajectory, and how complete trajectories are aggregated across a batch. We formulate these dimen- sions as Intra-Trajectory Feedback Attribution and Inter-Trajectory Objec- tive Aggregation, and introduce BATON (Bayesian Attribution and Trajectory Objective Normalization), a dual-axis policy optimization framework. BATON instantiates the first axis with Bayesian Feedback Attribution, which constructs a feedback-conditioned posterior over sampled actions, and the second with Trajec- tory Mass Normalization (TMN), which assigns equal optimization mass to com- plete trajectories. Experiments with GRPO and GiGPO on ALFWorld, WebShop, and SearchQA show that both axes provide independent gains and that their combi- nation consistently achieves the strongest overall performance across model scales.

Key facts. Cited by 1 paper 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 27 of its 35 references that are in the map. First posted on arXiv 17 Sep 2026.

Builds on

27 of 35 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Reconciling Process Supervision with Outcome-Based Credit in Agentic Policy Optimization31 Aug
  2. 2025Agent Lightning: Train ANY AI Agents with Reinforcement Learning5 Aug
  3. 2023Voyager: An Open-Ended Embodied Agent with Large Language Models25 May
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learning20 Mar

Other references · 23

  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. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  6. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
  7. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,444
  8. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  9. 2025Understanding R1-Zero-Like Training: A Critical Perspectivecited by 1,345
  10. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,251
  11. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
  12. 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,205
11 more
  1. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  2. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 574
  3. 2025SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wildcited by 511
  4. 2025Process Reinforcement through Implicit Rewardscited by 504
  5. 2025Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Modelcited by 461
  6. 2024AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agentscited by 339
  7. 2024WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learningcited by 256
  8. 2025ZeroSearch: Incentivize the Search Capability of LLMs without Searchingcited by 208
  9. 2025StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimizationcited by 88
  10. 2026Mitigating Manifold Departure: Uncertainty-Aware Subspace Rectification for Trustworthy MLLM Decodingcited by 4
  11. 2026SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognitioncited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DEEPO: Dual-Entropy Enhanced Policy Optimization for Hallucination in MLLMscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Reparameterized Policy Learning for Multimodal Trajectory Optimization
  2. 2026Trajectory-Aware Policy Optimization: Closing the State Distribution Gap in Long-Horizon LLM RL
  3. 2026T2SPO: Trajectory-to-Step Policy Optimization for Agentic Reinforcement Learning
  4. 2026Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning
  5. 2026Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents
  6. 2026Reflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias
  7. 2026Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
  8. 2026STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training