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A research paper shared as 'paper of the day' argues that a much smaller model can be preferred over one 100× larger when post-training teaches it to follow human intent.
This paper explores the challenges of verifying AI coding agents' outputs, arguing that verification is becoming harder than generation as models improve. It analyzes four reward constructions and shows that no fixed reward function remains effective as model capability grows.