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Really enjoyed COLM, feels like i don’t have to social with people for the next 3 months.

Happy Thanksgiving! Inspired by many great bloggers Sasha Rush Yao Fu, I made a tutorial about the "inference-time compute" tech showcased by O1. I incorporate insights from Sasha's great talk and ongoing O1 replications. Video: youtu.be/_Bw5o55SRL8. Feedback welcome!

What can AI researchers do *today* that AI developers will find useful for ensuring the safety of future advanced AI systems? To ring in the new year, the Anthropic Alignment Science team is sharing some thoughts on research directions we think are important.

If you can only read one DeepSeek paper in your life, read DeepSeek Math. Everything else is either ≈obvious in hindsight or clever optimization. DeepSeek Math is a tour de force of data engineering, general DL LLM methodology, RL, and just beautiful. Just 22 pages.

Last week we released s1 - our simple recipe for sample-efficient reasoning & test-time scaling. We’re releasing 𝐬𝟏.𝟏 trained on the 𝐬𝐚𝐦𝐞 𝟏𝐊 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 but performing much better by using r1 instead of Gemini traces. 60% on AIME25 I. Details in 🧵1/9

🚀 RAG vs. Long-Context LLMs: The Real Battle ⚔️ 🤯Turns out, simple-to-build RAG can match million-dollar long-context LLMs (LC LLMs) on most existing benchmarks. 🤡So, do we even need long-context models? YES. Because today’s benchmarks are flawed: ⛳ Too Simple –

Reasoning models often waste tokens self-doubting. Dynasor saves you up to 81% tokens to arrive at the correct answer! 🧠✂️ - Probe the model halfway to get the certainty - Use Certainty to stop reasoning - 100% Training-Free, Plug-and-play 🎮Demo: hao-ai-lab.github.io/demo/dynasor-c…

🚀 Highly recommend checking out The Future of Language Models and Transformers workshops hosted by the Simons Institute for the Theory of Computing at UC Berkeley! This is an incredible opportunity to learn about cutting-edge LLM research directly from some of the most renowned experts in the field.

Giving your models more time to think before prediction, like via smart decoding, chain-of-thoughts reasoning, latent thoughts, etc, turns out to be quite effective for unblocking the next level of intelligence. New post is here :) “Why we think”: lilianweng.github.io/posts/2025-05-…

I just started my internship at Adobe today, I am in San Jose for the next few months, welcome to connect and chat!