New Podcast Episode - TMax: Closing the Frontier Gap With Open Data

AI2 (the Allen Institute for AI) released TMax, a recipe for terminal agents that boosted a Qwen model's Terminal-Bench score from roughly 25% to 31%, closing ground on Claude Haiku's roughly 33%, by open-sourcing data, code, and technique rather than training a larger model.

This week, Cooper and Yash Neurometric AI unpack TMax, a paper out of AI2 the Allen Institute for AI that takes a genuinely different approach to closing the gap between small open-weight models and frontier labs — not by training a bigger model, but by open-sourcing the entire recipe: data, code, and technique. The paper focuses on terminal agents — models given nothing but command-line access as a tool, which turns out to be enough to tackle an enormous range of tasks, from software engineering to security work to scheduling. Using this recipe, AI2 boosted a Qwen model’s Terminal-Bench score from roughly 25% to 31%, closing meaningful ground on Claude Haiku’s roughly 33%. What made this one worth an episode wasn’t just the score bump. It was the design choices behind it. Watch on YouTube: https://youtu.be/4U0gqv9sSn4?si=ejv04gktGbTOMJM8 https://youtu.be/4U0gqv9sSn4?si=ejv04gktGbTOMJM8 Listen: https://tokenengineering.podbean.com https://tokenengineering.podbean.com/ Paper: TMax: A recipe for terminal agents — https://arxiv.org/abs/2606.23321 https://arxiv.org/abs/2606.23321