I'm a PhD student at the University of Washington advised by Abhishek Gupta, where I am supported by the NSF Graduate Research Fellowship. I'm interested in building reliable and efficiently adaptable systems via large-scale RL.
Previously, I did my undergrad at UC Berkeley advised by Sergey Levine and Aviral Kumar.
Research
Writing
Sep 2026 Progressive Point Matching How can we scalably train LLMs with RL toward long-horizon tasks? We propose a simple dense credit assignment method that generalizes imitation learning and standard outcome-level RL.
Sep 2025 Scaling Laws for Value-Based RL With the right design decisions, value-based reinforcement learning admits predictable scaling.
May 2024 Berkeley CS 184: Computer Graphics and Imaging All things graphics: rasterization, filtering and sampling, geometry, materials, light transport and image synthesis, cameras, physical simulation, virtual reality, etc. (I was a teaching assistant for this course and helped redesign lots of discussions!)
Dec 2023 Berkeley CS 285: Deep Reinforcement Learning Graduate course on reinforcement learning, control, and deep learning. Imitation learning; model-free, model-based, and offline algorithms; multitask learning; next steps.
Aug 2021 Covering Spaces The fundamental group, covering maps and topology, lifting properties of paths and homotopies, and the universal cover.
Jun 2020 Matrix Lie Groups An introduction to Lie theory through matrix Lie groups: exponentiation, tangent spaces, algebras, homomorphisms, and Baker–Campbell–Hausdorff formula.