A System One Model for Fast and Generalizable Decision-Making
Jacky Kwok $^{\dagger}$, Hangoo Kang , Tarun Suresh , Jon Saad-Falcon , Marco Pavone
Christopher Ré , Azalia Mirhoseini :stanford:Stanford University :nvidia:NVIDIA Research
New Architecture, Data Recipe, and Scaling Laws
We introduce Contrastive Language Models (CLMs), a new class of System One model trained with a contrastive learning objective that connects states and actions .
We release CLM-8B , which is pre-trained on 60M Nemotron Q&A pairs , mid-trained on 30M synthetic hard negatives , and post-trained on 1M agentic trajectories .
CLM-8B delivers performance comparable to Jev across computer-use, gaming, and tool-calling tasks, while achieving up to 9× lower latency . With lightweight fine-tuning, CLM-8B also sets a new SOTA on challenging agentic coding benchmarks, including DeepSWE (81.6%) and Terminal Bench 2.1 (87.6%).
We build an ultra-efficient training and serving infra for CLM by disaggregating states and actions , allowing their embeddings to be cached and reused independently.
We establish scaling laws for CLMs and show that the test contrastive loss decreases predictably as a power law in training compute , model size , and dataset size .
Overview
CLM first trains a state encoder and an action encoder on a large-scale dataset with a contrastive objective (InfoNCE), so that each state is pulled toward the ground truth action that was taken and pushed away from all others. The two encoders then serve directly as a zero-shot action classifier .
At deployment, given the current state and a set of candidate actions, CLM scores each action by how well its embedding aligns with the state embedding and selects the highest-scoring action.
Dino Run (CLM vs. Jev)
Zero-shot Evaluation
Across computer-use, gaming, and tool-calling tasks , CLM-8B performs on par with Jev while running up to 9× faster . The speedups are most pronounced when the number of candidate actions is large (e.g., WikiRacing) or when actions can be frequently reused across states (e.g., T-Rex Game).