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arXiv Machine Learning By Youling Huang, Guanqiao Chen, Junchi Yao, Lu Wang, Fangkai Yang, Chao Du, ChenZhuo Zhao, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Beyond State Consistency: Behavior Consistency in Text-Based World Models

Read the original on arXiv Machine Learning →

The paper introduces a new training paradigm for text-based world models that prioritizes behavior consistency over traditional state consistency metrics. It proposes the Behavior Consistency Reward (BehR), a step-level metric that evaluates how the likelihood of a logged next action changes between real and predicted states using a frozen Reference Agent. Experiments on WebShop and TextWorld demonstrate that BehR-based training improves long-term alignment, reduces false positives in offline evaluation, and yields modest gains in lookahead planning while maintaining or enhancing single-step prediction quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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