We've pushed a version update to the Terminal-Bench dataset and leaderboard.
Terminal-Bench 4.0 calibrates task resources (time, cpu, memory), implements task fixes, and removes saturated tasks.
We're releasing Terminal-Bench-Science: a benchmark for evaluating AI agents on research workflows across scientific domains.
An ongoing Stanford-led community effort, built by the team behind Terminal-Bench together with scientific domain experts at research institutions
Very excited to release the next project in the DataComp / OpenThoughts line of research! Like OpenThoughts we worked on post-training data, this time with a focus on agentic models.
How can we train small agentic models that are highly capable of terminal use and coding?
Announcing OpenThoughts-Agent + OpenThinkerAgent-32B, the strongest Qwen-3 based open-data agentic model: 44.8% avg across 7 agentic benchmarks! (1/n)
I'm a big fan of the approach to research funding @andykonwinski and the Laude team are taking! Working with them on terminal-bench has been fantastic (thanks @alexgshaw!) and I'm excited that they're going to support more open, impact-oriented research.
Today, I’m launching a deeply personal project. I’m betting $100M that we can help computer scientists create more upside impact for humanity.
Built for and by researchers, including @JeffDean & @jpineau1 on the board, @LaudeInstitute catalyzes research with real-world impact.
Very excited to finally release our paper for OpenThoughts!
After DataComp and DCLM, this is the third large open dataset my group has been building in collaboration with the DataComp community. This time, the focus is on post-training, specifically reasoning data.
