Reward models are metrics in disguise
Different labels, same pitfalls.
This position paper argues that reward models (for RL-based LLM training) and evaluation metrics face overlapping challenges—spurious correlations, reward hacking, data quality, and meta-evaluation. In some tasks, metrics even outperform reward models.
Why it matters: Aligning these research communities could improve preference elicitation, robustness to spurious signals, and calibration-aware evaluation.
It’s two sides of the same coin; flip it wisely. 🪙🧠🔍
Explore the survey and proposed research directions—then share where unifying efforts could help most.
Paper: http://arxiv.org/abs/2510.03231v1
Register: https://www.AiFeta.com
Paper: http://arxiv.org/abs/2510.03231v1
Register: https://www.AiFeta.com
#LLM #RLHF #Evaluation #AIAlignment #Metrics #RewardModels #MLResearch
