6 citations · 19 across the 15 of their papers we have counts for
4 papers · 1 filter
Stabilizing Policy Optimization via Logits Convexity
Hongzhan Chen, Tao Yang, Yuhua Zhu +3
While reinforcement learning (RL) has been central to the recent success of large language models (LLMs), RL optimization is notoriously unstable, especially when compared to super…
Merge and Guide: Unifying Model Merging and Guided Decoding for Controllable Multi-Objective Generation
Guofu Xie, Chen Zhang, Xiao Zhang +3
Adapting to diverse user needs at test time is a key challenge in controllable multi-objective generation. Existing methods are insufficient: merging-based approaches provide indir…
Learning More with Less: A Dynamic Dual-Level Down-Sampling Framework for Efficient Policy Optimization
Chao Wang, Tao Yang, Hongtao Tian +5
Critic-free methods like GRPO reduce memory demands by estimating advantages from multiple rollouts but tend to converge slowly, as critical learning signals are diluted by an abun…
Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generation
Guofu Xie, Xiao Zhang, Ting Yao +1
User information needs are often highly diverse and varied. A key challenge in current research is how to achieve controllable multi-objective generation while enabling rapid adapt…