Staff Research Scientist · RL & Systems for LLM Agents
My research focuses on scalable reinforcement learning systems for large language models, across algorithms and training systems. I currently work on reasoning, self-improvement, efficient on-policy training at scale, long-horizon credit assignment in multi-turn RLVR, memory-augmented agents that improve through open-ended interaction, and cross-domain generalisation.
Good research and good engineering belong together: reproducible training recipes, reliable evaluations, and open-source systems that make ideas inspectable are all part of the same ecosystem.
Selected publications
Reproducible multi-domain RLVR recipe for a 15B open-weight model, with adaptive domain sampling and a difficulty-aware length penalty for stronger, shorter reasoning.
Read paperAsynchronous RL infrastructure with in-flight weight updates for fast long-sequence generation while keeping training data near on-policy.
Read paperBenchmark and RL framework for agents that must balance task success with privacy leakage from external research queries over multi-hop local and web evidence.
Read paperA 15B supernet that supports multiple decoding speed-quality presets from one checkpoint, with released models, serving code, and placement tooling.
Read paperTape-centered agent design for resumable state, debugging, evaluation, fine-tuning, prompt tuning, and reusable agent traces.
Read paperEarlier work spans implicit offline RL, target-network regularization, active learning, and practical ML workflows for high-stakes investigation settings.
Research systems
Distributed asynchronous reinforcement learning framework for long-horizon LLM training, with in-flight weight updates, multi-domain rollouts, tool use, and scalable post-training workflows.
Framework for building, debugging, serving, and optimizing LLM agents through structured, replayable tapes that connect engineering traces back to model improvement.
Evaluation and training infrastructure for long-horizon LLM agents, focused on repeatable measurement, agentic tasks, and systems that improve through interaction.
Blog posts
Research taste
Engineering lens
My background combines applied AI research with production software, distributed systems, networking, and infrastructure. I am most interested in research ideas that can be made concrete: implemented, measured, debugged, scaled, and released in a form other people can build on.