来源简介
arXiv:2610.25086v1 Announce Type: new Abstract: Looped language models increase computational depth by repeatedly applying shared layers, offering a way to improve model quality without adding parameters. However, sequential dependencies between loop iterations increase autoregressive decoding latency. We introduce Dual-Slot, an architecture that reorganizes serial looped computation into parallel latent and prediction slots. Rather than waiting for the current token's latent computation to finish, the prediction slot combines the previous token's deep latent state with the current representation, while the latent slot produces a state for the next decoding step. This shifts the latent dependency across adjacent tokens, replacing two sequential module invocations with a single paired invocation while preserving standard autoregressive generation. To preserve token-level parallelism during training, we use a small number of Jacobi iterations to refine latent states across the sequence in parallel. Experiments on Qwen-style dense and mixture-of-experts (MoE) models show consistent improvements in language modeling and average downstream accuracy. On the 1B dense model, Dual-Slot reduces Pile validation perplexity from 7.25 to 7.00 and improves average five-shot accuracy across nine task configurations by 2.26 percentage points. After supervised fine-tuning, it improves zero-shot GSM8K accuracy by 5.83 and 3.49 percentage points for the 1B dense and A0.6B MoE models, respectively. Across the evaluated batch sizes, Dual-Slot retains 93.1-96.5% of vanilla decoding throughput for the dense model and 98.1-99.6% for the MoE model.
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采集于 2026/10/20 21:06:31（北京时间）。 arXiv 日期为本次公告时间，论文初次投稿日期请查看原文版本记录。