8 citations · 9 across the 5 of their papers we have counts for
5 papers
VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators
Hengtao Li, Pengxiang Ding, Runze Suo +8
Vision-Language-Action (VLA) models enable embodied decision-making but rely heavily on imitation learning, leading to compounding errors and poor robustness under distribution shi…
Score and Distribution Matching Policy: Advanced Accelerated Visuomotor Policies via Matched Distillation
Bofang Jia, Pengxiang Ding, Can Cui +5
Visual-motor policy learning has advanced with architectures like diffusion-based policies, known for modeling complex robotic trajectories. However, their prolonged inference time…
CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision
Hao Wang, Zeyu Gao, Chao Zhang +7
Binary code representation learning has shown significant performance in binary analysis tasks. But existing solutions often have poor transferability, particularly in few-shot and…
Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality
Yaqing Hou, Mingyang Sun, Abhishek Gupta +4
In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not signifi…
ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning
Linkang Du, Min Chen, Mingyang Sun +4
Data is a critical asset in AI, as high-quality datasets can significantly improve the performance of machine learning models. In safety-critical domains such as autonomous vehicle…