#reward models

RewardBench offers a comprehensive framework for assessing reward model capabilities and safety, with support for Direct Preference Optimization (DPO). Featuring inference scripts for models like Starling and PairRM, and tools for analyzing and visualizing results, RewardBench ensures efficient model testing and logging. The command line interface facilitates easy setup, and compatibility with various dataset formats makes it a vital tool for researchers seeking accurate reward model evaluation.