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The results suggest that temporal thinking offers a parameter-efficient alternative to increasing physical depth in recurrent Transformers, and that temporal thinking offers a parameter-efficient alternative to increasing physical depth in recurrent Transformers.
Latent Recurrent Transformer is studied, a lightweight augmentation of autoregressive transformers that reuses a high-level source-layer hidden state from the previous token as recurrent memory for the next token, while retaining one-forward-per-token decoding with 9% latency overhead over the standard Transformer.
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