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

4 code implementations • 8 Apr 2024 • 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., Niklas Muennighoff, Fares Obeid, Atsushi Saito, Guangyu Song, Haoqin Tu, Stanisław Woźniak, Ruichong Zhang, Bingchen Zhao, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu

We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.

Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent

no code implementations • 13 Jul 2023 • Ruichong Zhang

Even fewer algorithms are compatible with gradient descent, the common learning process for neural networks.

Distributional Reinforcement Learning

RWKV: Reinventing RNNs for the Transformer Era

5 code implementations • 22 May 2023 • Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Jiaju Lin, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartlomiej Koptyra, Hayden Lau, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Bolun Wang, Johan S. Wind, Stanislaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Qinghua Zhou, Jian Zhu, Rui-Jie Zhu

This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.

Computational Efficiency Natural Language Inference