Today OpenAI announced o3, its next-gen reasoning model. We've worked with OpenAI to test it on ARC-AGI, and we believe it represents a significant breakthrough in getting AI to adapt to novel tasks. It scores 75.7% on the semi-private eval in low-compute mode (for $20 per task

Prediction: one day, the idea of training human-readable reasoning traces will seem bizarre. (It's always seemed bizarre to me at least -- kinda like requiring a diffusion image model to output an image sequence that matches the strokes an artist would use.)

Introducing *ARC‑AGI Without Pretraining* – ❌ No pretraining. ❌ No datasets. Just pure inference-time gradient descent on the target ARC-AGI puzzle itself, solving 20% of the evaluation set. 🧵 1/4

One of my PhD students got their visa revoked. I know of other cases amongst my AI colleagues. This is not what investing in US leadership in AI looks like.

Nous Research's RL Environments Hackathon recap thread! Starting with the stars of the show, the winners! Top 3 for the subjective track were: 1st - Pokemon Trainer - by Ajay Uppili Arasanipalai & Alex Reibman 🖇️ 2nd - VR-CLImax by Jake Boggs 3rd - DynastAI by David van Vliet and

🚀 Excited to share the most inspiring work I’ve been part of this year: "Learning to Reason without External Rewards" TL;DR: We show that LLMs can learn complex reasoning without access to ground-truth answers, simply by optimizing their own internal sense of confidence. 1/n

I explored Lei Yang's Discrete Distribution Networks in the tiny corp, a generative approach using discrete nodes instead of diffusion noise. Blog + code: alexanderspeicher.com/blog/discrete-… Paper: arxiv.org/abs/2401.00036