21 citations · 23 across the 31 of their papers we have counts for
10 papers · 1 filter
Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry
Guoxi Zhang, Jiawei Chen, Tianzhuo Yang +4
As Large Language Models (LLMs) expand in capability and application scope, their trustworthiness becomes critical. A vital risk is intrinsic deception, wherein models strategicall…
Enhance the Safety in Reinforcement Learning by ADRC Lagrangian Methods
Mingxu Zhang, Huicheng Zhang, Jiaming Ji +2
Safe reinforcement learning (Safe RL) seeks to maximize rewards while satisfying safety constraints, typically addressed through Lagrangian-based methods. However, existing approac…
Your Group-Relative Advantage Is Biased
Fengkai Yang, Zherui Chen, Xiaohan Wang +10
Reinforcement Learning from Verifier Rewards (RLVR) has emerged as a widely used approach for post-training large language models on reasoning tasks, with group-based methods such…
J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge
Chi-Min Chan, Chunpu Xu, Jiaming Ji +7
The current focus of AI research is shifting from emphasizing model training towards enhancing evaluation quality, a transition that is crucial for driving further advancements in…
Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization
Juntao Dai, Taiye Chen, Yaodong Yang +2
Reinforcement learning from human feedback (RLHF) is an effective method for aligning large language models (LLMs) with human values. However, reward over-optimization remains an o…
Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback
Jiaming Ji, Xinyu Chen, Rui Pan +13
Multimodal large language models (MLLMs) are essential for building general-purpose AI assistants; however, they pose increasing safety risks. How can we ensure safety alignment of…