Wednesday, 5th March 2025
QwQ-32B: Embracing the Power of Reinforcement Learning (via) New Apache 2 licensed reasoning model from Qwen:
We are excited to introduce QwQ-32B, a model with 32 billion parameters that achieves performance comparable to DeepSeek-R1, which boasts 671 billion parameters (with 37 billion activated). This remarkable outcome underscores the effectiveness of RL when applied to robust foundation models pretrained on extensive world knowledge.
I had a lot of fun trying out their previous QwQ reasoning model last November. I demonstrated this new QwQ in my talk at NICAR about recent LLM developments. Here's the example I ran.
LM Studio just released GGUFs ranging in size from 17.2 to 34.8 GB. MLX have compatible weights published in 3bit, 4bit, 6bit and 8bit. Ollama has the new qwq too - it looks like they've renamed the previous November release qwq:32b-preview.
Career Update: Google DeepMind -> Anthropic. Nicholas Carlini (previously) on joining Anthropic, driven partly by his frustration at friction he encountered publishing his research at Google DeepMind after their merge with Google Brain. His area of expertise is adversarial machine learning.
The recent advances in machine learning and language modeling are going to be transformative [d] But in order to realize this potential future in a way that doesn't put everyone's safety and security at risk, we're going to need to make a lot of progress---and soon. We need to make so much progress that no one organization will be able to figure everything out by themselves; we need to work together, we need to talk about what we're doing, and we need to start doing this now.
Demo of ChatGPT Code Interpreter running in o3-mini-high. OpenAI made GPT-4.5 available to Plus ($20/month) users today. I was a little disappointed with GPT-4.5 when I tried it through the API, but having access in the ChatGPT interface meant I could use it with existing tools such as Code Interpreter which made its strengths a whole lot more evident - that’s a transcript where I had it design and test its own version of the JSON Schema succinct DSL I published last week.
Riley Goodside then spotted that Code Interpreter has been quietly enabled for other models too, including the excellent o3-mini reasoning model. This means you can have o3-mini reason about code, write that code, test it, iterate on it and keep going until it gets something that works.
Code Interpreter remains my favorite implementation of the "coding agent" pattern, despite recieving very few upgrades in the two years after its initial release. Plugging much stronger models into it than the previous GPT-4o default makes it even more useful.
Nothing about this in the ChatGPT release notes yet, but I've tested it in the ChatGPT iOS app and mobile web app and it definitely works there.
The Graphing Calculator Story (via) Utterly delightful story from Ron Avitzur in 2004 about the origins of the Graphing Calculator app that shipped with many versions of macOS. Ron's contract with Apple had ended but his badge kept working so he kept on letting himself in to work on the project. He even grew a small team:
I asked my friend Greg Robbins to help me. His contract in another division at Apple had just ended, so he told his manager that he would start reporting to me. She didn't ask who I was and let him keep his office and badge. In turn, I told people that I was reporting to him. Since that left no managers in the loop, we had no meetings and could be extremely productive