8 papers
Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling
Yiran Guo, Zhongjian Qiao, Yingqi Xie +5
Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast n…
Leveraging Verifier-Based Reinforcement Learning in Image Editing
Hanzhong Guo, Jie Wu, Jie Liu +6
While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing remains largely unexplored. A k…
DTP: A Simple yet Effective Distracting Token Pruning Framework for Vision-Language Action Models
Chenyang Li, Jieyuan Liu, Bin Li +5
Vision-Language Action (VLA) models have shown remarkable progress in robotic manipulation by leveraging the powerful perception abilities of Vision-Language Models (VLMs) to under…
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…
GRPO-Guard: Mitigating Implicit Over-Optimization in Flow Matching via Regulated Clipping
Jing Wang, Jiajun Liang, Jie Liu +10
Recently, GRPO-based reinforcement learning has shown remarkable progress in optimizing flow-matching models, effectively improving their alignment with task-specific rewards. With…
Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance
Luozhijie Jin, Zijie Qiu, Jie Liu +5
Denoising-based generative models, particularly diffusion and flow matching algorithms, have achieved remarkable success. However, aligning their output distributions with complex…