I want language models that don't just recall what we know, but help discover what we don't.
My research is on the post-training of language models — the data and algorithms that turn a pretrained model into a capable, reliable reasoner and agent. I work from a data-centric view: much of what looks like a modeling problem is really a question of which experience a model learns from, and how.
That lens runs through my work. I've shown that instruction diversity — not sheer volume — drives generalization; that the best supervised fine-tuning prepares a model for reinforcement learning rather than merely imitating it; and that self-improving agents can quietly corrupt their own memory as they accumulate experience. The connecting thread is understanding the mechanisms behind generalization well enough to engineer it.
Looking forward, I'm most excited about agents that move from recalling knowledge to discovering it: offline-to-online RL, incentivizing proactive reasoning for knowledge discovery, and foundation-model agents that can be dropped into a novel environment and learn it by experiment. The next frontier for post-training, I believe, is building models that extend the frontier of human knowledge — not just compress it.
Post-training data & algorithms
What data and objectives actually make models generalize — instruction diversity, SFT-for-RL, data selection & reweighting.
RL & reasoning
Offline-to-online reinforcement learning and incentivizing proactive, verifiable reasoning behaviors.
Self-improving agents
How agents learn from their own experience — and the failure modes when memory is continually rewritten.
AI for scientific discovery
Agents that experiment, form hypotheses, and recover mechanisms — a step toward AI scientists.
What's new
Read my research, the fun way
Visual, scrollable companions to my papers — built to be read in about ten minutes.
CausaLab: Can LLM Agents Discover Causal Mechanisms by Experiment?
Agents in a synthetic lab — intervening, observing, revising. They predict the right answer with the wrong mechanism, and stop experimenting too soon.
Read write-up →Useful Memories Become Faulty When Continuously Updated by LLMs
Agents that compress experience into textual lessons can end up worse than the same model with no memory at all — even on problems they already solved.
Read write-up →GridRule: Self-Proposed Subgoal RL in an ARC-AGI-3-Style Environment
A pilot study — can a 0.8B model learn to decompose multi-step problems by proposing its own subgoals? Compositional generalization, 1.8× baseline, replicated on two seeds.
Read write-up →