Agent-Editing World Model: Rethinking World Modeling for LLM Agents
Researchers propose Agent-Editing World Model to revise noisy agent reasoning instead of predicting tool responses.
The paper introduces the Agent-Editing World Model, which edits noisy reasoning and actions rather than predicting high-entropy tool responses. An Action Judge separates Critical, Exploratory, and Noisy decisions, and State Revision rewrites contaminated continuations from the same history. Trained on Search, Terminal, and Software Engineering, it reaches 70.5% macro-F1, beating the strongest frontier baseline by 10.6 points. EditAct then improves average scores by 3.2–6.7 points, and rejection-sampling fine-tuning adds 2.2–2.6 points over Self-RFT.
- AEWM models task progress instead of reconstructing tool responses.
- Action Judge labels decisions Critical, Exploratory, or Noisy.
- Action Judge scores 70.5% macro-F1, 10.6 points above the best baseline.
- EditAct gains 3.2–6.7 points across six benchmarks and three backbones.
- AEWM-RFT beats Self-RFT by 2.2–2.6 points without online guidance.
From the original article
1 min read at arxiv.orgRecent advances in large language models (LLMs) have enabled agents to tackle long-horizon tasks across diverse environments. To further improve agent performance, existing language world models typically predict environment observations, yet reconstructing high-entropy, execution-dependent tool responses offers limited value when real feedback is available.
By Shuang Sun · arxiv.org
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