1 citations · 2 across the 8 of their papers we have counts for
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Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance
Zhuo Li, Pengyu Cheng, Zhechao Yu +7
Reward models (RMs) are essential in reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human values. However, RM training data is commonl…
APLOT: Robust Reward Modeling via Adaptive Preference Learning with Optimal Transport
Zhuo Li, Yuege Feng, Dandan Guo +3
The reward model (RM) plays a crucial role in aligning Large Language Models (LLMs) with human preferences through Reinforcement Learning, where the Bradley-Terry (BT) objective ha…
Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm
Zhuo Li, Yuhao Du, Xiaoqi Jiao +5
Selecting high-quality and diverse training samples from extensive datasets plays a crucial role in reducing training overhead and enhancing the performance of Large Language Model…
RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment
Yuhao Du, Zhuo Li, Pengyu Cheng +4
Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning Large Language Models (LLMs) with human values. However, RLHF has been continuously challenged by its high…