RWKV-7 "Goose" with Expressive Dynamic State Evolution

BlinkDL, Ruichong Zhang, Dan Goldstein, Eric Alcaide, howard-hou, Janna, William Merrill, Guangyu Song, Kaifeng Tan, saitejautpala, Nathan Wilce, Johan S. Wind, Tianyi Wu, Dr. Daniel Wuttke, Christian Zhou-Zheng

RWKV-7 "Goose" with Expressive Dynamic State Evolution: 154 upvotes on Hugging Face Daily Papers, #1 of 31 papers on 2025-03-19. Day-by-day upvote history.

We present RWKV-7 "Goose", a new sequence modeling architecture, along with pre-trained language models that establish a new state-of-the-art in downstream performance at the 3 billion parameter scale on multilingual tasks, and match current SoTA English language performance despite being trained on dramatically fewer tokens than other top 3B models. Nevertheless, RWKV-7 models require only constant memory usage and constant inference time per token. RWKV-7 introduces a newly generalized formulation of the delta rule with vector-valued gating and in-context learning rates, as well as a relaxed value replacement rule. We show that RWKV-7 can perform state tracking and recognize all regular languages, while retaining parallelizability of training. This exceeds the capabilities of Transformers under standard complexity conjectures, which are limited to TC^0. To demonstrate RWKV-7's language modeling capability, we also present an extended open source 3.1 trillion token multilingual corpus, and train four RWKV-7 models ranging from 0.19 billion to 2.9 billion parameters on this dataset. To foster openness, reproduction, and adoption, we release our models and dataset component listing at https://huggingface.co/RWKV, and our training and inference code at https://github.com/RWKV/RWKV-LM all under the Apache 2.0 License.

Paper page on Hugging Face · arXiv

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