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cs.LG2025
Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment
Shengyang Sun, Yian Zhang, Alexander Bukharin +11
The rapid development of large language model (LLM) alignment algorithms has resulted in a complex and fragmented landscape, with limited clarity on the effectiveness of different…
cs.LG2024
HelpSteer2-Preference: Complementing Ratings with Preferences
Zhilin Wang, Alexander Bukharin, Olivier Delalleau +5
Reward models are critical for aligning models to follow instructions, and are typically trained following one of two popular paradigms: Bradley-Terry style or Regression style. Ho…