Stanford's CLM: an 8B System 1 model with 9x faster inference, new agentic coding SOTA
StanfordAILab · x · 2026-09-25
A Stanford team (Hazy Research and collaborators) introduced the Contrastive Language Model (CLM), an ultra-fast System 1 model connecting states and actions via contrastive learning:
- CLM-8B, pre-trained on internet-scale data, delivers up to 9x faster inference than Jev with comparable performance on computer use, gaming, and tool calling
- With lightweight fine-tuning it sets new SOTA on agentic coding benchmarks: DeepSWE 81.6% and Terminal-Bench 2.1 87.6%; Jev fails to serve as an effective verifier for these long-horizon tasks
- The team also built efficient training/serving infrastructure by disaggregating states and actions, and established scaling laws across compute, model size, and dataset size
Code, data, and models are released openly.
More from coding & agent
- Pigment Agents now reads and posts in Slack, inheriting user permissions — mattturck · 2026-11-12
- Map of the agent economy: 50+ startups across payments, spend control, identity, memory — venturetwins · 2026-11-12
- OpenCode launches Browse, an on-device browser agent with local logins — Saul_Loveman · 2026-11-12
- VentureTwins maps the B2A economy: dozens of agent infra and app companies in first series installment — venturetwins · 2026-11-12
- Google releases 76-page 'Agents Companion' whitepaper on agent engineering — mdancho84 · 2026-11-12
- Spinning up containers for 20-line agent tools is backwards: Safescript compiles provable, 50ms-deploy apps — uriwa · 2026-11-12