RLVR-World: Training World Models with Reinforcement Learning

Jialong Wu, Joseph Yin, Ningya Feng, Mingsheng Long

RLVR-World: Training World Models with Reinforcement Learning: 17 upvotes on Hugging Face Daily Papers, #18 of 42 papers on 2025-05-22. Day-by-day upvote history.

World models predict state transitions in response to actions and are increasingly developed across diverse modalities. However, standard training objectives such as maximum likelihood estimation (MLE) often misalign with task-specific goals of world models, i.e., transition prediction metrics like accuracy or perceptual quality. In this paper, we present RLVR-World, a unified framework that leverages reinforcement learning with verifiable rewards (RLVR) to directly optimize world models for such metrics. Despite formulating world modeling as autoregressive prediction of tokenized sequences, RLVR-World evaluates metrics of decoded predictions as verifiable rewards. We demonstrate substantial performance gains on both language- and video-based world models across domains, including text games, web navigation, and robot manipulation. Our work indicates that, beyond recent advances in reasoning language models, RLVR offers a promising post-training paradigm for enhancing the utility of generative models more broadly.

Paper page on Hugging Face · arXiv

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