3 papers
cs.LG2026
Beyond Binary Preferences: A Principled Framework for Reward Modeling with Ordinal Feedback
Amirhossein Afsharrad, Ruida Zhou, Luca Viano +2
Reward modeling is crucial for aligning large language models with human preferences, yet current approaches lack a principled mathematical framework for leveraging ordinal prefere…
cs.CL2026
DISPO: Enhancing Training Efficiency and Stability in Reinforcement Learning for Large Language Model Mathematical Reasoning
Batuhan K. Karaman, Aditya Rawal, Suhaila Shakiah +4
Reinforcement learning with verifiable rewards has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models particularly in mathematics. Cu…
cs.LG2026
Direct Preference Optimization with Rating Information: Practical Algorithms and Provable Gains
Luca Viano, Ruida Zhou, Yifan Sun +4
The class of direct preference optimization (DPO) algorithms has emerged as a promising approach for solving the alignment problem in foundation models. These algorithms work with…