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Leonard Tang

@leonardtang_4,344 subscribers

ceo @haizelabs

Shorts

First came pre-training scaling; then came inference-time scaling. Now comes judge-time scaling. Despite progress in AI through scaled inference-time compute, AI remains unreliable in open-ended, non-verifiable domains. The key limitation is not generation—it is evaluation. Therefore, the next big leap for AI comes from better judging. In service of this future, today we release Verdict, a library for scaling judge-time compute.

First came pre-training scaling; then came inference-time scaling. Now comes judge-time scaling. Despite progress in AI through scaled inference-time compute, AI remains unreliable in open-ended, non-verifiable domains. The key limitation is not generation—it is evaluation. Therefore, the next big leap for AI comes from better judging. In service of this future, today we release Verdict, a library for scaling judge-time compute.

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