1 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
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…
SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling
Xingzhou Lou, Junge Zhang, Jian Xie +3
Human preference alignment is critical in building powerful and reliable large language models (LLMs). However, current methods either ignore the multi-dimensionality of human pref…