Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior
We study Latent Recurrent Transformer (LRT), 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. Because this state is already computed during ordinary decoding, LRT introduces a cross-token, cross-layer latent pathway while preserving the standard attention mechanism, KV-cache interface, and one model forward per generated token. To pretrain this recurrence without sequentially unrolling the full sequence, we introduce interleaved parallel training: one full-sequence initialization forward constructs a shared buffer, followed by sequential refinement of disjoint positi...
| Authors | Zeyi Huang, Xuehai He, LiLiang Ren et al. |
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| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 1 September 2026 |
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