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Yuege Feng

2 accepted papers

2025

APLOT: Robust Reward Modeling via Adaptive Preference Learning with Optimal Transport

EMNLP 2025

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 has been recognized as simple yet powerful, specifically for pairwise preference learning. However, BT-based RMs often str

2025

Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm

EMNLP 2025

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 Models (LLMs). However, existing studies fall short in assessing the overall value of selected data, focusing primarily on in