10 papers
Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming
Baoshun Tong, Haoran He, Ling Pan +2
Vision-Language-Action (VLA) models have achieved remarkable success in robotic manipulation. However, their robustness to linguistic nuances remains a critical, under-explored saf…
Coherent and Multi-modality Image Inpainting via Latent Space Optimization
Lingzhi Pan, Tong Zhang, Bingyuan Chen +4
With the advancements in denoising diffusion probabilistic models (DDPMs), image inpainting has significantly evolved from merely filling information based on nearby regions to gen…
GARDO: Reinforcing Diffusion Models without Reward Hacking
Haoran He, Yuxiao Ye, Jie Liu +7
Fine-tuning diffusion models via online reinforcement learning (RL) has shown great potential for enhancing text-to-image alignment. However, since precisely specifying a ground-tr…
Steering Vision-Language-Action Models as Anti-Exploration: A Test-Time Scaling Approach
Siyuan Yang, Yang Zhang, Haoran He +4
Vision-Language-Action (VLA) models, trained via flow-matching or diffusion objectives, excel at learning complex behaviors from large-scale, multi-modal datasets (e.g., human tele…
Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards
Haoran He, Yuxiao Ye, Qingpeng Cai +4
RL with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving the reasoning abilities of large language models (LLMs). Current methods rely primarily on polic…
Random Policy Evaluation Uncovers Policies of Generative Flow Networks
Haoran He, Emmanuel Bengio, Qingpeng Cai +1
The Generative Flow Network (GFlowNet) is a probabilistic framework in which an agent learns a stochastic policy and flow functions to sample objects proportionally to an unnormali…