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By refining VLM-derived reward signals with structural priors, SAFT transforms noisy feedback into a reliable guide for faster and more aligned policy learning.
HarnessCompass boosts agent performance by 22% in just five iterations, setting a new standard for generalization in automatic harness evolution.
