5 citations · 8 across the 8 of their papers we have counts for
5 papers · 1 filter
Calibration-Aware Policy Optimization for Reasoning LLMs
Ziqi Wang, Xingzhou Lou, Meiqi Wu +2
Group Relative Policy Optimization (GRPO) enhances LLM reasoning but often induces overconfidence, where incorrect responses yield lower perplexity than correct ones, degrading rel…
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…
Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models
Xingzhou Lou, Junge Zhang, Ziyan Wang +2
Safe reinforcement learning (RL) agents accomplish given tasks while adhering to specific constraints. Employing constraints expressed via easily-understandable human language offe…