Contrastive-LM Releases CLM-8B: An Open System One Model That Scores Agent Actions Up to 9× Faster Than Jev
Contrastive-LM released CLM-8B, an open model that scores agent actions up to 9× faster than Jev.
Contrastive-LM released CLM-8B, an Apache-2.0 model that scores candidate actions rather than generating text, using a frozen Qwen3-8B encoder and a 75 MB head served with vLLM. Against TypeSafe AI’s proprietary Jev model, zero-shot latency was as much as about 9 times lower, including 16.5 ms versus 149.8 ms on a T-Rex task. Fine-tuned verifier heads reached 81.6% on a 38-task DeepSWE subset and 87.6% on 30 Terminal-Bench 2.1 tasks. Training used roughly 60 million Nemotron DQA pairs, 30 million Gemini 2.5 Flash-Lite hard negatives, and 1 million agent trajectories.
- CLM-8B scores candidate actions with probabilities instead of generating text.
- It uses frozen Qwen3-8B encoders and a 75 MB Apache-2.0 head.
- Zero-shot T-Rex latency is 16.5 ms versus 149.8 ms for Jev.
- Fine-tuned heads reach 81.6% on DeepSWE and 87.6% on Terminal-Bench subsets.
- Training used about 60M DQA pairs, 30M hard negatives, and 1M trajectories.
- Products
- vLLM
- Organizations
- Contrastive-LMTypeSafe AI
From the original article
3 min read at marktechpost.com
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.
By Michal Sutter · marktechpost.com
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