Improving Bilinear RNN with Closed-loop Control

Recent efficient sequence modeling methods, such as Gated DeltaNet, TTT, and RWKV-7, have achieved performance improvements by supervising the recurrent memory management through the Delta learning rule. Unlike previous state-space models (e. g. , Mamba) and gated linear attentions (e. g. , GLA), these models introduce interactions between the recurrent state and the key vector, resulting in a bilinear recursive structure. In this paper, we first introduce the concept of Bilinear RNNs with a comprehensive analysis on the advantages and limitations of these models. Then based on the closed-loop control theory, we propose a novel Bilinear RNN variant named Comba, which adopts a scalar-plus-low-rank state transition, with both state feedback and output feedback corrections. We also implement a hardware-efficient chunk-wise parallel kernel in Triton and train models with 340M/1. 3B parameters on a large-scale corpus. Comba demonstrates its superior performance and computation efficiency on both language modeling and vision tasks.

Authors

• Jiaxi Hu
• Yongqi Pan
• Jusen Du
• Disen Lan South China University of Technology
• Tang Tang
• Qingsong Wen Squirrel AI
• Yuxuan Liang The Hong Kong University of Science and Technology Chinese Academy of Sciences
• Weigao Sun Shanghai AI Laboratory

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