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Contrastive-LM introduces CLM-8B, an open-source alternative to proprietary System One models that accelerates agent action scoring by up to nine times through a contrastive learning architecture. By freezing the Qwen3-8B backbone and training lightweight projection heads, this approach offers significant efficiency gains for evaluating candidate actions without generating text. The model's Apache-2.0 licensing and compatibility with existing TypeSafe APIs make it an immediately deployable option for researchers and developers seeking high-performance agent verification.

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Contrastive-LM Releases CLM-8B: An Open System One Model That Scores Agent Actions Up to 9× Faster Than Jev

MarkTechPostSeptember 24, 20263 min read

Contrastive-LM has released CLM-8B, an open System One model that scores candidate actions against a state instead of generating text. It adds 2 small projection heads to a frozen Qwen3-8B encoder and trains them with a contrastive InfoNCE objective. In zero-shot tests it runs up to 9× faster than TypeSafe's Jev. With fine-tuned heads as a verifier, it reaches 81.6% on held-out DeepSWE tasks and 87.6% on held-out Terminal-Bench 2.1 tasks. The post Contrastive-LM Releases CLM-8B: An Open System One Model That Scores Agent Actions Up to 9× Faster Than Jev appeared first on MarkTechPost.

Contrastive-LM has released CLM-8B, the first open model in a new class called Contrastive Language Models (CLMs). CLM does not generate text. It scores a set of candidate actions against the current state and returns probabilities. Their main baseline is Jev, the proprietary System One model from TypeSafe AI. Is it deployable? Yes. The Apache-2.0 head weighs 75 MB.…

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