Diagnosing Component-Level Failures in Computer-Use Agents
COLM 2026 (In review)Tianchen Guan, Daisy Xinlei Lin, Xiangjun Wang, Shuyan Zhou
Research Scientist & Engineer — Amazon AGI SF Lab
I work on RL post-training for AI agents. At Amazon AGI SF Lab, I worked on the end-to-end RL training recipe for Nova Act — Amazon's production browser-use agent — and build evaluation frameworks for agentic systems.
My PhD at NYU was about how humans plan and learn: I ran large-scale experiments to understand what makes people get better at complex tasks, and how that compares to AI.
I think AI should be humans' thought partners — adapting to collaborate with us better and better. I also think they should be beautiful.
Owned the end-to-end RL training recipe for Nova Act. Designed and shipped an online data curator that improved RL sample efficiency. Diagnosed and resolved a training/inference mismatch that stabilized RL training at scale across agentic tasks. Drove measurable gains in training data diversity and reliability.
Tianchen Guan, Daisy Xinlei Lin, Xiangjun Wang, Shuyan Zhou
Daisy Xinlei Lin, Brenden Lake, Wei Ji Ma
PaperVeronica Yeom-Song, Xinlei Lin, Ionatan Kuperwajs, Heiko Schütt, Wei Ji Ma, Luigi Acerbi
Zheyang Sam Zheng*, Xinlei Daisy Lin*, Jake Topping*, Wei Ji Ma
PaperJordan Lei, Jeroen Olieslagers, Nastaran Arfaei, Xinlei Lin, Wei Ji Ma
PaperEnida Gjoni, Ram Dyuthi Sristi, Haixin Liu, Shahar Dror, Xinlei Lin, Keelin O'Neil, Oscar M. Arroyo, Sun Woo Hong, Hannah Kim, Jeffrey Liu, Sonja Blumenstock, Byungkook Lim, Gal Mishne, Takaki Komiyama
PaperI snowboard when I can — Hokkaido is the dream — bake when I need to think, make ceramics, and am currently being managed by two cats named Boba and Milk who take their supervisory roles extremely seriously.
People ask about my skincare routine more than my research, which I find extremely funny. Happy to talk about both.