Your AI models are failing in production—Here’s how to fix model selection

Enterprises need to know if the models that power their applications and agents work in real-life scenarios. This type of evaluation can sometimes be complex because it is hard to predict specific scenarios. A revamped version of the RewardBench benchmark looks to give organizations a better idea of a model’s real-life performance. 

The Allen Institute of AI (Ai2) launched RewardBench 2, an updated version of its reward model benchmark, RewardBench, which they claim provides a more holistic view of model performance and assesses how models align with an enterprise’s goals and standards. 

Ai2 built RewardBench with classification tasks that measure correlations through inference-time compute and downstream training. RewardBench mainly deals with reward models (RM), which can act as judges and evaluate LLM outputs. RMs assign a score or a “reward” that guides reinforcement learning with human feedback (RHLF).

RewardBench 2 is here! We ...