@inproceedings{huang-etal-2026-latent,
title = "Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior",
author = "Huang, Zeyi and
He, Xuehai and
Ren, Liliang and
Wang, Yiping and
Peng, Baolin and
Cheng, Hao and
Wang, Shuohang and
He, Pengcheng and
Gao, Jianfeng and
Lee, Yong Jae and
Shen, Yelong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2281/",
pages = "48860--48879",
ISBN = "979-8-89176-462-0",
abstract = "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 \textit{interleaved parallel training}: one full-sequence initialization forward constructs a shared buffer, followed by sequential refinement of disjoint position subsets with parallel computation within each subset. This provides every token with recurrent-memory-aware supervision at approximately 2x ideal token compute. Across 1.3B- and 2.1B-parameter nanochat-style backbones and a wide range of training budgets, LRT improves both BPB and CORE under matched effective compute. Additionally, LRT outperforms two-forward PonderLM-2 and matches a three-loop Transformer in BPB, while retaining one-forward-per-token decoding with 9{\%} latency overhead over the standard Transformer."
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<abstract>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 position subsets with parallel computation within each subset. This provides every token with recurrent-memory-aware supervision at approximately 2x ideal token compute. Across 1.3B- and 2.1B-parameter nanochat-style backbones and a wide range of training budgets, LRT improves both BPB and CORE under matched effective compute. Additionally, LRT outperforms two-forward PonderLM-2 and matches a three-loop Transformer in BPB, while retaining one-forward-per-token decoding with 9% latency overhead over the standard Transformer.</abstract>
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%0 Conference Proceedings
%T Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior
%A Huang, Zeyi
%A He, Xuehai
%A Ren, Liliang
%A Wang, Yiping
%A Peng, Baolin
%A Cheng, Hao
%A Wang, Shuohang
%A He, Pengcheng
%A Gao, Jianfeng
%A Lee, Yong Jae
%A Shen, Yelong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F huang-etal-2026-latent
%X 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 position subsets with parallel computation within each subset. This provides every token with recurrent-memory-aware supervision at approximately 2x ideal token compute. Across 1.3B- and 2.1B-parameter nanochat-style backbones and a wide range of training budgets, LRT improves both BPB and CORE under matched effective compute. Additionally, LRT outperforms two-forward PonderLM-2 and matches a three-loop Transformer in BPB, while retaining one-forward-per-token decoding with 9% latency overhead over the standard Transformer.
%U https://aclanthology.org/2026.findings-emnlp.2281/
%P 48860-48879
Markdown (Informal)
[Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior](https://aclanthology.org/2026.findings-emnlp.2281/) (Huang et al., Findings 2026)
ACL
- Zeyi Huang, Xuehai He, Liliang Ren, Yiping Wang, Baolin Peng, Hao Cheng, Shuohang Wang, Pengcheng He, Jianfeng Gao, Yong Jae Lee, and Yelong Shen. 2026. Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 48860–48879, Budapest, Hungary. Association for Computational Linguistics.