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cs.LG2026
Bayesian Preference Learning for Test-Time Steerable Reward Models
Jiwoo Hong, Shao Tang, Zhipeng Wang
Reward models are central to aligning language models with human preferences via reinforcement learning (RL). As RL is increasingly applied to settings such as verifiable rewards a…
cs.LG2025
Aligning Diffusion Language Models via Unpaired Preference Optimization
Vaibhav Jindal, Hejian Sang, Chun-Mao Lai +2
Diffusion language models (dLLMs) are an emerging alternative to autoregressive (AR) generators, but aligning them to human preferences is challenging because sequence log-likeliho…
cs.LG2025
Debunk the Myth of SFT Generalization
Xiaofeng Lin, Hejian Sang, Zhipeng Wang +1
A prevailing view holds that supervised fine-tuning (SFT) memorizes training data and fails to generalize, whereas reinforcement learning (RL) attains broader robustness. We revisi…