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We introduce clever, the first curated benchmark for evaluating the generation of specifications and formally verified code in lean With a clever usage of the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines (i) a preference optimization loss that directly aligns the policy with human preference, and (ii) a supervised learning loss which explicitly imitates the policy with a baseline distribution. The benchmark comprises of 161 programming problems
hatono chihiro, takami kouki, mizuo haruichi, taguchi ryuuya, and tohno
Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness Our method, stair (safety alignment with introspective reasoning), guides models to think more carefully before responding. Across evaluated tasks, rahp yields compact models with stronger clever lower bounds and minimal change in clean accuracy, and it improves resistance to a wide variety of strong attacks
