Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946. For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids. An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better. This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.

Lots of people interested in the late Interaction workshop, listening to @lateinteraction's keynote!

The recipe: 1. Solve Search. 2. Use it to solve everything else.

This "Aha moment" in the DeepSeek-R1 paper is huge: Pure reinforcement learning (RL) enables an LLM to automatically learn to think and reflect. This challenges the prior belief that replicating OpenAI's o1 reasoning models requires extensive CoT data. It turns out you just need to give it the right incentives. We are so back in the AlphaGo excitement era: by playing countless Go games and maximizing the reward function (winning the game) using pure RL, AlphaGo beat the best human players. Now we are entering the LLM RL era. 2025 could be the year of RL.

As we begin the new year, Horses New Year’s Feast is our #FilmoftheWeek, a short film showing the animals at the Home Of Rest For Horses during the annual New Year feast at a charity event on Westcroft Farm, Cricklewood in 1913. buff.ly/3PwMAuo @BFI

How to train a 670B parameter model. Let's talk about the DeepSeek v3 report some comparisons with what Meta did with Llama 405B

I'll get straight to the point. We trained 2 new models. Like BERT, but modern. ModernBERT. Not some hypey GenAI thing, but a proper workhorse model, for retrieval, classification, etc. Real practical stuff. It's much faster, more accurate, longer context, and more useful. 🧵