CLM-8B picks actions by embedding similarity and generates no text at all.

Separate state and action encoders trained with an InfoNCE objective, scoring candidate actions by embedding alignment. Pre-trained on 60M Nemotron Q&A pairs, post-trained on 1M agentic trajectories. The team reports Jev-level zero-shot results on computer use, games and tool calling, and after fine-tuning, 81.6% on DeepSWE and 87.6% on Terminal-Bench 2.1 (Contrastive Language Models). Action embeddings cache, so latency claims run 4-13x better. The coding numbers need independent replication before anyone builds on them.