3 papers
cs.AI2026
Token-Importance Guided Direct Preference Optimization
Ning Yang, Hai Lin, Yibo Liu +3
Aligning Large Language Models (LLMs) with human preferences is crucial for safe and effective AI interactions. While popular methods like Direct Preference Optimization (DPO) have…
cs.CL2025
Evolving LLMs' Self-Refinement Capability via Synergistic Training-Inference Optimization
Yongcheng Zeng, Xinyu Cui, Xuanfa Jin +11
Self-Refinement refers to a model's ability to revise its own responses to produce improved outputs. This capability can also serve as a fundamental mechanism for Self-Improvement,…
cs.LG2025
Proactive Constrained Policy Optimization with Preemptive Penalty
Ning Yang, Pengyu Wang, Guoqing Liu +3
Safe Reinforcement Learning (RL) often faces significant issues such as constraint violations and instability, necessitating the use of constrained policy optimization, which seeks…