Author

Shi-Long Zhou

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Preprint Sep 2026

EVO-WAM: Evolving World Action Models through Video-Action Verification

Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning. World action models (WAMs) use broad video priors to jointly predict future videos and actions, offering a potential source of supervision for adapting to new tasks. However, generate...

Shi-Long Zhou, Xiong-Hao Wu, Wen-Bo Li et al. · 0 citations

Preprint Aug 2026

ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training

This work introduces ZimaBlue, a scalable framework for learning generalizable World Action Models (WAMs) from large-scale video, and adopts an asynchronous Slow-Fast dual-system architecture to make generative WAMs practical for real-time control.

Xiong-Hao Wu, Yi-Jun Yang, Shi-Long Zhou et al. · 5 citations

Conference Open access Sep 2026

A Survey of Joint Online-Offline Fine-tuning for Large Language Models

This work advocates for Joint Online-Offline Fine-Tuning as a superior paradigm that breaks the convention of restricting offline data to SFT and online data to RFT, and provides the first comprehensive survey focusing specifically on the synchronization of data provenance.

Tai-Hang Zhen, Guang Yang, Chen-Zhang Li et al. · Proceedings of the Thirty-Fi... · 0 citations