Zhengyao Jiang

Heard about autoresearch, or tried it a few times in playground settings? This hands-on tutorial teaches you how to use autoresearch on one of the most serious challenges in ML this year: OpenAI's Parameter Golf. The challenge: train the best language model that fits in just 16MB. We entered our autoresearch agent this past spring, and it outperformed the field of over 1,000 participants. You'll learn how we approached it, then get to do it yourself: kick off an autoresearch agent, watch it improve a tiny language model's training script, steer it when progress stalls, and visualize your results. You'll leave with a working autoresearch setup you can point at your own code. compute kindly sponsored by Modal! Speakers: Zhengyao Jiang — Weco AI; Dixing Xu — Weco AI; Vayum Arora — Weco AI; Dhruv Srikanth — Weco AI.

Earlier this year, OpenAI ran Parameter Golf, a model-training competition that doubled as a hiring filter. Over 1,000 researchers competed to train the best small language model under a 16MB cap. The top contributor was the one candidate OpenAI couldn't hire. Our autonomous research agent Aiden finished with 7 merged records, more than twice as many as any other contributor, and ended up the most-cited participant in the community. This talk is about what those 22 days showed. I'll cover on high level how does it works and which of its ideas produced the records. But the part worth more than the leaderboard is the collaboration itself, the community and AI agent building on each other's work, the largest natural experiment in human-AI collaboration I've seen run in public. I'll close with what it tells us about where humans and autonomous research each still matter for the foreseeable future. 1:57 PM