4 sources
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.
Researcher Profile
Editor reviewedFerdinand Mom
RWKV and efficient sequence modeling
RWKV contributor focused on efficient sequence models
A useful RWKV page because his work does not stop at the original paper; it extends into multimodal and longer-context experiments that show how the RWKV line kept evolving afterward.
Organizations
Nexen
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
Known For
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01
RWKV recurrent sequence models
02
RWKV-CLIP and multimodal extensions
03
Long-context efficient-sequence experimentation
04
RWKV and efficient sequence modeling
05
RWKV: Reinventing RNNs for the Transformer Era
06
RWKV (project)
Start Here
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Signature Works
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Supporting Sources
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Related Researchers
People worth exploring next because they share topics, labs, or source material with this profile.
5 sources
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.
Start HereEric Alcaide
5 sources
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.
Start HereAlon Albalak
2 sources
Co-authored RWKV: Reinventing RNNs for the Transformer Era.
4 sources
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.
Start HereHuanqi Cao at Tsinghua University
2 sources
Co-authored RWKV: Reinventing RNNs for the Transformer Era.