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CLM-8B uses contrastive state-action learning for agent tasks
CLM-8B adds state and action projection heads to a frozen Qwen3-8B encoder, trained with bidirectional InfoNCE; its zero-shot results are reported as on par with Jev on several agent tasks at up to 9× lower latency. The approach and latency claim are worth examining for teams building fast tool-use or computer-use systems.
- What
- CLM-8B is a contrastive language model for ranking and agent tasks, built on a frozen Qwen3-8B encoder.
- How
- Two projection heads encode states and actions, trained with a bidirectional InfoNCE loss; separately encoded actions can be reused for caching.
- Why it matters
- Reusable action embeddings can speed candidate ranking, and the model can also serve as a verifier for agent tasks.
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