Ok, so I can finally talk about this! We spent the last year (actually a bit longer) training an LLM with recurrent depth at scale. The model has an internal latent space in which it can adaptively spend more compute to think longer. I think the tech report ...🐦‍⬛

New open source reasoning model! Huginn-3.5B reasons implicitly in latent space 🧠 Unlike O1 and R1, latent reasoning doesn’t need special chain-of-thought training data, and doesn't produce extra CoT tokens at test time. We trained on 800B tokens 👇

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach 🚀🚀🚀 arxiv.org/abs/2502.05171

I'm at NeurIPS in Vancouver right now! Feel free to reach out to talk about anything in LLM safety or efficiency research. Also, our new ELLIS institute Tübingen is hiring new faculty, the deadline is next week - reach out to us in person and at our booth for more info 🇪🇺🇪🇺🇪🇺