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Stanford/NVIDIA researchers unveil CLM, a contrastive-learning model for fast agent action selection

Research team publishes CLM details and benchmark results

2 Today 12:20 AM · 5h ago · 1 post · 1 source · development 2 of 3

A Notion page from the Stanford/NVIDIA team (Jacky Kwok, Hangoo Kang, Tarun Suresh, Jon Saad-Falcon, Marco Pavone, Christopher Ré, Azalia Mirhoseini) explains CLM's contrastive state/action encoder design and reports CLM-8B matching Jev's performance at up to 9× the speed across computer-use, gaming, and tool-calling tasks; the writeup circulates on Hacker News.

Research team publishes CLM details and benchmark results
x.com

“Across computer-use, gaming, and tool-calling tasks, CLM-8B performs on par with Jev while running up to 9× faster.”

Stanford/NVIDIA research team, Paper authors · hn ↗

Jacky Kwok Lead researcher, Stanford UniversityChristopher Ré Senior co-author, Stanford UniversityAzalia Mirhoseini Senior co-author, Stanford University / NVIDIA ResearchHangoo Kang Co-author, Stanford UniversityTarun Suresh Co-author

The whole story posts the bright band is this development · numbered dots are the others · click one to jump

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1Jacky Kwok announces CLM-8B on X2Research team publishes CLM details and…3Coverage of CLM spreads further on Hacker…
8 PM10 PMtoday2 AM4 AMnow · 6:10 AM ET

Reported in the same hours no headline names this development itself — these 1 claim were published in its stretch

  1. 1 outlet Contrastive-LM Releases CLM-8B: An Open System One Model That Scores Agent Actions Up to 9× Faster Than Jev

    first by MarkTechPost, 4h ago

All 3 developments of Stanford/NVIDIA researchers unveil CLM, a… →

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