19 citations · 57 across the 6 of their papers we have counts for
12 papers
MoDoMoDo: Multi-Domain Data Mixtures for Multimodal LLM Reinforcement Learning
Yiqing Liang, Jielin Qiu, Wenhao Ding +7
Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a powerful paradigm for post-training large language models (LLMs), achieving state-of-the-art perform…
Behavior Injection: Preparing Language Models for Reinforcement Learning
Zhepeng Cen, Yihang Yao, William Han +2
Reinforcement learning (RL) has emerged as a powerful post-training technique to incentivize the reasoning ability of large language models (LLMs). However, LLMs can respond very i…
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training
Yihang Yao, Zhepeng Cen, Miao Li +6
Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. However, even minor variations in query phrasing, despite preserving the underlyi…
Robustness Certification of Visual Perception Models via Camera Motion Smoothing
Hanjiang Hu, Zuxin Liu, Linyi Li +2
A vast literature shows that the learning-based visual perception model is sensitive to adversarial noises, but few works consider the robustness of robotic perception models under…
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability
Mengdi Xu, Zuxin Liu, Peide Huang +4
A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} cons…
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
Baiming Chen, Zuxin Liu, Jiacheng Zhu +3
Safety is a critical concern when deploying reinforcement learning agents for realistic tasks. Recently, safe reinforcement learning algorithms have been developed to optimize the…