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20242026
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cs.CL2025

AlphaPO: Reward Shape Matters for LLM Alignment

Aman Gupta, Shao Tang, Qingquan Song +10

Reinforcement Learning with Human Feedback (RLHF) and its variants have made huge strides toward the effective alignment of large language models (LLMs) to follow instructions and…

cs.CL2025

On the Robustness of Reward Models for Language Model Alignment

Jiwoo Hong, Noah Lee, Eunki Kim +5

The Bradley-Terry (BT) model is widely practiced in reward modeling for reinforcement learning with human feedback (RLHF). Despite its effectiveness, reward models (RMs) trained wi…

cs.CL2025

The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models

Seungone Kim, Juyoung Suk, Ji Yong Cho +29

As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently as…

cs.CL2025

Cross-lingual Transfer of Reward Models in Multilingual Alignment

Jiwoo Hong, Noah Lee, Rodrigo Martínez-Castaño +2

Reinforcement learning with human feedback (RLHF) is shown to largely benefit from precise reward models (RMs). However, recent studies in reward modeling schemes are skewed toward…

cs.CL2024

Evaluating the Consistency of LLM Evaluators

Noah Lee, Jiwoo Hong, James Thorne

Large language models (LLMs) have shown potential as general evaluators along with the evident benefits of speed and cost. While their correlation against human annotators has been…