Author

Xiaoyi Pang

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Preprint Sep 2026

SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models

World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous semantic streams of video, proprioceptive, and action tokens. Quantization reduces inference cost, but comparable numerical errors in different streams can have markedly...

Yun-Han Wang, Hao-Dong Wang, Zhi-Ming Liu et al. · 0 citations

#robotics Preprint Jul 2026

RedFlow: Redirect Failure into Action-Level Corrections for Flow-matching VLA Policy

Flow-matching Vision-Language-Action (VLA) policies have shown strong potential for robotic manipulation but often suffer from compounding errors caused by distribution shifts during deployment. While offline reinforcement learning (RL) provides a practical way to improve deployed policies using rollout data, existing...

Zhengyang Yan, Junhao Li, Fangqi Zhu et al. · 2 citations

Preprint Aug 2026

WorldCycle: Self-Verifiable Reinforcement Learning for Long-Horizon Video World Models

WorldCycle is introduced, a self-verifiable RL framework that constructs closed action cycles and their repeated executions from ordinary action sequences, and optimizes two complementary rewards: a spatial closure reward enforcing symmetry between mirrored forward and reverse segments, and a temporal consistency rewar...

Bohai Gu, Yueyang Yuan, Tai-Yi Wu et al. · 2 citations