New arXiv preprint! "On the Optimal Reasoning Length for RL-Trained Language Models"
Two failure modes in RL-trained reasoning: long outputs increase dispersion, short outputs cause under-thinking.
This tradeoff can be monotonic or non-monotonic depending on the model.
We’ve developed a new way to train small AI models with internal mechanisms that are easier for humans to understand.
Language models like the ones behind ChatGPT have complex, sometimes surprising structures, and we don’t yet fully understand how they work.
This approach helps us begin to close that gap.
https://t.co/g4zOcdezPU
The Illustrated NeurIPS 2025: A Visual Map of the AI Frontier
New blog post!
NeurIPS 2025 papers are out—and it’s a lot to take in. This visualization lets you explore the entire research landscape interactively, with clusters, summaries, and @cohere LLM-generated explanations that make the field easier to grasp.
Link in thread!