author
Research Scientist
,
Snorkel AI
I am an aspiring AI researcher with a diverse range of experience in frontier AI research, large scalable machine learning systems, and applied analytics in social science. I believe in the interactionist approach to intelligence development, through granular feedbacks from grounded, open-ended environments, where robust rewards are essential to forge systems that learn, adapt, and evolve through interactions.
The latest from Jason
Blog
Milestone-Based Evaluation and Training for Long-Horizon AI Agents
Long-horizon agents are outgrowing pass/fail evaluation. A short task can be scored as one outcome. A longer workflow is different. The agent may plan correctly, navigate to the right place, call the right tool, and still fail during execution. A final failure score does not show which phase broke. Training has the same shape of problem. Reinforcement learning for long-horizon…
Blog
Cua-Bench: benchmarking computer-use agents on professional software
TL;DR We built a benchmark of 25 expert-authored KiCad schematic-editing tasks and ran a frontier computer-use agent against them. The headline numbers: 1. Why build a computer-use benchmark for electrical engineering? Most computer-use benchmarks today live in the same handful of apps: web browsers, file managers, generic productivity suites. Those evaluations are useful, but they share a structural weakness —…
