Agent-Editing World Model: Rethinking World Modeling for LLM Agents
AEWM edits contaminated agent state and improves six benchmarks by 3.2 to 6.7 points over the strongest baseline.
The Agent-Editing World Model predicts how reasoning and actions change future task progress instead of reconstructing high-entropy tool responses. It pairs an Action Judge, which labels Critical, Exploratory, and Noisy decisions, with State Revision that edits noisy continuations from the same history. Trained on Search, Terminal, and Software Engineering, it scores 70.5% macro-F1, 10.6 points above the strongest frontier baseline. EditAct then improves average scores by 3.2 to 6.7 points across six benchmarks and three agent backbones.
- AEWM models task progress rather than reconstructing tool responses.
- Action Judge separates Critical, Exploratory, and Noisy decisions.
- Action Judge reaches 70.5% macro-F1, 10.6 points above the best frontier baseline.
- EditAct improves average scores by 3.2 to 6.7 points across six benchmarks.
- AEWM-RFT beats Self-RFT by 2.2 to 2.6 points without online guidance.
From the original article
1 min read at huggingface.co
Recent 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, Guoxin Chen, Fanzhe Meng · huggingface.co
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