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cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…