5 papers
Uncertainty-aware Reward Model: Teaching Reward Models to Know What is Unknown
Xingzhou Lou, Dong Yan, Wei Shen +3
Reward models (RMs) are essential for aligning large language models (LLM) with human expectations. However, existing RMs struggle to capture the stochastic and uncertain nature of…
3D-Properties: Identifying Challenges in DPO and Charting a Path Forward
Yuzi Yan, Yibo Miao, Jialian Li +4
Aligning large language models (LLMs) with human preferences has gained significant attention, with Proximal Policy Optimization (PPO) as a standard yet computationally expensive m…
Exploring the LLM Journey from Cognition to Expression with Linear Representations
Yuzi Yan, Jialian Li, Yipin Zhang +1
This paper presents an in-depth examination of the evolution and interplay of cognitive and expressive capabilities in large language models (LLMs), with a specific focus on Baichu…
Boosting Deductive Reasoning with Step Signals In RLHF
Jialian Li, Yipin Zhang, Wei Shen +3
Logical reasoning is a crucial task for Large Language Models (LLMs), enabling them to tackle complex problems. Among reasoning tasks, multi-step reasoning poses a particular chall…
Reward-Robust RLHF in LLMs
Yuzi Yan, Xingzhou Lou, Jialian Li +6
As Large Language Models (LLMs) continue to progress toward more advanced forms of intelligence, Reinforcement Learning from Human Feedback (RLHF) is increasingly seen as a key pat…