Ph.D. student, Stanford AI
I am a final-year Ph.D. student at Stanford University, advised by Chelsea Finn and Aviral Kumar, and a Research Scientist Intern at Meta MSL. Prior to Stanford, I was at UC Berkeley, where I was advised by Sergey Levine, and I have held internships at Microsoft Research NYC, Google DeepMind Robotics, and Toyota Research Institute. My research is supported by the NSF Graduate Research Fellowship.
My research develops algorithms for self-improving and creative foundation models, with the long-term goal of enabling progress on difficult, open-ended problems such as scientific research. These problems require more than imitating demonstrations or solving well-specified tasks: models must decide what is worth trying, synthesize partial evidence, discover useful abstractions, and refine ideas whose value is revealed only through downstream validation. I study how feedback from a model's own attempts, natural-language critiques, and inference-time computation can be transformed into learning signals that drive exploration and improvement. The aim is to build models that can generate, evaluate, and refine novel insights, learning problem-solving strategies that generalize across domains.
ICLR 2026 · Spotlight, ES-FoMo Workshop @ ICML 2025
A multi-agent RL method (RLAD) that trains LLMs to discover reusable reasoning abstractions, steering exploration toward useful strategies on hard problems.
Oral Presentation, LM4Sci Workshop @ CoLM 2026
An LM trained with RL to anticipate a paper's core insight from its foundational references, generalizing to unseen scientific disciplines.
Runner-Up Best Paper, Reasoning and Planning for LLMs Workshop @ ICLR 2025
Pairs process verifiers with preemptive backtracking to resample only problematic steps, cutting test-time compute.
CoLM 2025 · Oral Presentation
Shows that intrinsic reasoning behaviors — verification, backtracking, subgoal setting, and backward chaining — are what let LLMs self-improve under reinforcement learning.
Preprint 2025
Extends chain-of-thought by explicitly modeling the latent reasoning behind each CoT, via process supervision, synthetic data, and search.
Preprint 2024
A generative self-evaluation scheme that lets an LLM predict mid-generation whether restarting would yield a better response, cutting sampling cost.
CoLM 2025
Over 250,000 high-quality, verifiable math problems built for reinforcement learning in language models.
ICLR 2026
FSPO, a meta-learning framework that leverages in-context learning to personalize LLM responses to individual users from only a few preferences.
ICML 2024
Shows that on-policy sampling and negative-gradient methods outperform offline maximum-likelihood objectives for preference fine-tuning.
TMLR 2026
HyRe adapts a multi-head model at test time from about five labeled examples, matching state-of-the-art reward models at the cost of fine-tuning one.
CVPR 2025
Personalizes text-to-image diffusion models from a few examples via user preference embeddings and cross-attention during DPO fine-tuning.
RSS 2023
Combines offline-RL pre-training on diverse robot data with rapid fine-tuning, learning new tasks from as few as 10 demonstrations.
NeurIPS 2023
A calibrated, conservative value initialization that enables fast and stable offline-to-online fine-tuning.
NeurIPS 2023
Reworks the CQL distribution constraint into an approximate support constraint, enabling offline RL on realistic, heteroskedastic data.