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The arXiv paper "CleanRM" uses a small amount of clean preference data to calibrate the noise label distribution and then trains a reward model with a noise-corrected loss, enabling teams to obtain reward model quality close to clean annotation on cheap but noisy crowdsourced annotations or weak LLM reviewer labels.
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10/16/2026
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