RWKV Architecture: How Receptance Weighted Key Value Decay Combines RNN Efficiency with Transformer Parallelizability
The dominant paradigm in natural language processing relies on the Transformer architecture, which calculates scaled dot-product self-attention across all token pairs in a sequence. While self-attention provides strong in-context retrieval and representation capacity, it imposes quadratic computational and memory complexity, scaling as O(N^2) with sequence length N during training and generating a continuously expanding Key-Value (KV) cache during autoregressive inference. Traditional Recurrent
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