19 citations · 45 across the 21 of their papers we have counts for
5 papers · 1 filter
Skywork Open Reasoner 1 Technical Report
Jujie He, Jiacai Liu, Chris Yuhao Liu +14
The success of DeepSeek-R1 underscores the significant role of reinforcement learning (RL) in enhancing the reasoning capabilities of large language models (LLMs). In this work, we…
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…
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…
Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation
Xiaoying Zhang, Jean-Francois Ton, Wei Shen +2
We introduce Adversarial Policy Optimization (AdvPO), a novel solution to the pervasive issue of reward over-optimization in Reinforcement Learning from Human Feedback (RLHF) for L…
Improving Generalization of Alignment with Human Preferences through Group Invariant Learning
Rui Zheng, Wei Shen, Yuan Hua +9
The success of AI assistants based on language models (LLMs) hinges crucially on Reinforcement Learning from Human Feedback (RLHF), which enables the generation of responses more a…