1 citations · 2 across the 11 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2025
Towards Reward Fairness in RLHF: From a Resource Allocation Perspective
Sheng Ouyang, Yulan Hu, Ge Chen +3
Rewards serve as proxies for human preferences and play a crucial role in Reinforcement Learning from Human Feedback (RLHF). However, if these rewards are inherently imperfect, exh…
cs.LG2024
Video-Text Dataset Construction from Multi-AI Feedback: Promoting Weak-to-Strong Preference Learning for Video Large Language Models
Hao Yi, Qingyang Li, Yulan Hu +3
High-quality video-text preference data is crucial for Multimodal Large Language Models (MLLMs) alignment. However, existing preference data is very scarce. Obtaining VQA preferenc…
cs.LG2024
TSO: Self-Training with Scaled Preference Optimization
Kaihui Chen, Hao Yi, Qingyang Li +4
Enhancing the conformity of large language models (LLMs) to human preferences remains an ongoing research challenge. Recently, offline approaches such as Direct Preference Optimiza…