Author

Qi Wang

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Preprint Sep 2026

HaWMPO: Hallucination-Aware World Model-based Policy Optimization for Generalist Robot Policy

Generalist robot policies have demonstrated strong generalization across robotic manipulation tasks, yet their success rates remain limited in com- plex long-horizon scenarios. Recent methods improve Visual-Language-Action (VLA) policies through online reinforcement learning on real robots, but such training relies on...

Ze Chen, Pei-Dong Liu, Jiawei Li et al. · 0 citations

#artificial intelligence Jun 2026

Policy and World Modeling Co-Training for Language Agents

PaW is a Policy and World modeling co-training framework that adds auxiliary WM supervision to the same policy during RL, without changing the inference paradigm, and shows consistent improvements over strong RL baselines across models and RL algorithms.

Ning Lu, Baijiong Lin, Sheng-Cai Liu et al. · arXiv.org · 12 citations · ⚡2

Preprint Aug 2026

Beyond Flat Policies: Hierarchical Post-Training for Embodied Agents in Robotic Manipulation

Hierarchical Robotic Control (HiRoC) is proposed, a hierarchical post-training framework that decouples high-level task planning from low-level action execution and aligns the executor with planner-generated subgoals before reinforcement learning, mitigating the distribution misalignment between planning and execution.

He Kong, Ze Chen, Qi Wang et al. · 0 citations