12 papers
AcceRL: A Distributed Asynchronous Reinforcement Learning and World Model Framework for Vision-Language-Action Models
Chengxuan Lu, Shukuan Wang, Yanjie Li +10
Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models is severely bottlenecked by synchronization barriers and the high cost of environment data acquisiti…
UDM-GRPO: Stable and Efficient Group Relative Policy Optimization for Uniform Discrete Diffusion Models
Jiaqi Wang, Haoge Deng, Ting Pan +5
Uniform Discrete Diffusion Model (UDM) has recently emerged as a promising paradigm for discrete generative modeling; however, its integration with reinforcement learning remains l…
Labeled TrustSet Guided: Batch Active Learning with Reinforcement Learning
Guofeng Cui, Yang Liu, Pichao Wang +4
Batch active learning (BAL) is a crucial technique for reducing labeling costs and improving data efficiency in training large-scale deep learning models. Traditional BAL methods o…
Enhancing Reinforcement Learning Fine-Tuning with an Online Refiner
Hao Ma, Zhiqiang Pu, Yang Liu +1
Constraints are essential for stabilizing reinforcement learning fine-tuning (RFT) and preventing degenerate outputs, yet they inherently conflict with the optimization objective b…
Skywork UniPic 3.0: Unified Multi-Image Composition via Sequence Modeling
Hongyang Wei, Hongbo Liu, Zidong Wang +11
The recent surge in popularity of Nano-Banana and Seedream 4.0 underscores the community's strong interest in multi-image composition tasks. Compared to single-image editing, multi…
Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model
Hongyang Wei, Baixin Xu, Hongbo Liu +18
Recent advances in multimodal models have demonstrated impressive capabilities in unified image generation and editing. However, many prominent open-source models prioritize scalin…