Researcher Profile
Editor reviewedRWKV and efficient sequence modeling
Research scientist on the Open-Endedness team at Lila Sciences
A strong open-model and data-centric page because his work sits close to the infrastructure that made OLMo and Dolma useful to the broader research community rather than just another benchmark-driven model release.
Organizations
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last reviewed
March 18, 2026
Official And External Links
The ideas, systems, and research directions that make this person worth knowing.
01
Open language models and open pretraining corpora
02
Data-centric AI and data selection for language models
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Research infrastructure for transparent model development
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RWKV and efficient sequence modeling
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RWKV: Reinventing RNNs for the Transformer Era
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RWKV (project)
Canonical papers, project pages, or repositories that anchor this profile.
Additional papers, projects, or repositories that help flesh out the profile.
Additional links that help verify and flesh out this profile.
People worth exploring next because they share topics, labs, or source material with this profile.
A useful long-tail page because he is present on both the original RWKV paper and Eagle/Finch, then shows up again on multilingual embedding evaluation work, which makes him more than a one-paper launch contributor.
Worth keeping because it connects an early RWKV byline to a much more visible later research program in agentic AI, biomedical discovery, and code-focused evaluation, which makes the page far more useful than a one-paper ghost profile.
Worth tracking if you care about alternatives to the standard transformer playbook, especially the line of work trying to keep strong language-model performance while making inference and memory use much cheaper.
A distinctive page because his work bridges open-sequence-model experimentation with applied machine learning for molecules, proteins, and structural biology, and he shows up on multiple RWKV-family papers including the hybrid GoldFinch branch rather than only the first release.
Co-authored RWKV: Reinventing RNNs for the Transformer Era.
Useful because it turns an otherwise thin RWKV byline into a real systems profile: after the original paper, his public work tracks toward large-scale pretraining infrastructure, pipeline parallelism, and systems support for frontier-scale models.