6 papers
Learn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs
Wenqi Chen, Ziyan Zhang, Bin Wang +3
While Large Language Models (LLMs) excel in code generation, they remain prone to replicating subtle yet critical vulnerabilities endemic to their training data. Current alignment…
FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization
Yihao Wu, He Zhang, Junbo Tan +2
Post-training Vision-Language-Action (VLA) models into policies that can be reliably deployed on real robots remains a major bottleneck. SFT and DAgger exploit failure signals only…
PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models
Ziyang Chen, Shaoguang Wang, Weiyu Guo +5
Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation. However, deploying these models in open-ended environments requir…
Tri-Prompting: Video Diffusion with Unified Control over Scene, Subject, and Motion
Zhenghong Zhou, Xiaohang Zhan, Zhiqin Chen +8
Recent video diffusion models have made remarkable strides in visual quality, yet precise, fine-grained control remains a key bottleneck that limits practical customizability for c…
GoldenStart: Q-Guided Priors and Entropy Control for Distilling Flow Policies
He Zhang, Ying Sun, Hui Xiong
Flow-matching policies hold great promise for reinforcement learning (RL) by capturing complex, multi-modal action distributions. However, their practical application is often hind…
CHOICE: Coordinated Human-Object Interaction in Cluttered Environments for Pick-and-Place Actions
Jintao Lu, He Zhang, Yuting Ye +3
Animating human-scene interactions such as pick-and-place tasks in cluttered, complex layouts is a challenging task, with objects of a wide variation of geometries and articulation…