Author

Zi-Lang Cen

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Preprint Sep 2026

What Matters in Designing World Action Models: An Empirical Study

World Action Models (WAMs) have emerged as a promising paradigm for generalizable robot control. Despite the growing number of WAM systems, existing works often introduce unified systems that bundle together multiple design choices, such as architecture and training strategy, making it difficult to isolate individual c...

Chao Tang, Haoqing Wang, Zi-Lang Cen et al. · 0 citations

Preprint Oct 2026

eRLT: Efficient VLA Reinforcement Learning via Action-Relevant Token Routing

Vision-Language-Action (VLA) models provide strong behavioral priors for robotic manipulation, yet efficiently adapting them to downstream tasks remains challenging. Recent work addresses this challenge by adapting frozen VLAs through online reinforcement learning (RL), whose sample efficiency depends on the quality of...

De-Hao Huang, Jian-Bang Liu, Jian-Pan Gao et al. · 0 citations