Summary of Eagle and Finch: Rwkv with Matrix-valued States and Dynamic Recurrence, by Bo Peng et al.

Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence

by Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak, Eric Alcaide, Stella Biderman, Eugene Cheah, Xingjian Du, Teddy Ferdinan, Haowen Hou, Przemysław Kazienko, Kranthi Kiran GV, Jan Kocoń, Bartłomiej Koptyra, Satyapriya Krishna, Ronald McClelland Jr., Jiaju Lin, Niklas Muennighoff, Fares Obeid, Atsushi Saito, Guangyu Song, Haoqin Tu, Cahya Wirawan, Stanisław Woźniak, Ruichong Zhang, Bingchen Zhao, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu

First submitted to arxiv on: 8 Apr 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper presents two sequence models, Eagle (RWKV-5) and Finch (RWKV-6), which improve upon the RWKV (RWKV-4) architecture. The advancements include multi-headed matrix-valued states and a dynamic recurrence mechanism that enhance expressivity while maintaining inference efficiency characteristics of RNNs. A new multilingual corpus with 1.12 trillion tokens is introduced, along with a fast tokenizer based on greedy matching for enhanced multilinguality. Four Eagle models and two Finch models are trained, ranging from 0.46 to 7.5 billion parameters, achieving competitive performance across various benchmarks. The paper releases all models under the Apache 2.0 license.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper introduces two new sequence models that improve upon previous designs. The models use a combination of techniques to enhance their expressiveness and efficiency. A large multilingual corpus is used to train the models, which are then tested on various benchmarks. The results show that the models perform well across different tasks and datasets.