LSTM-Style Looped Transformers with Shared KV Caches

Andreas Kirsch

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arXiv · 2610.33360

LSTM-Style Looped Transformers with Shared KV Caches

Andreas Kirsch

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Abstract

Recurrent transformers reuse weights to perform deep computation with fewer parameters. We study an LSTM-style transformer that also shares one key--value cache across its recurrent core. At width 1024, it uses 40.4% fewer parameters than an untied control at nearly matched forward FLOPs, and reduces persistent cache sets from fifteen to four. Parallel training supplies cache entries from a standard-attention pass, whereas continuation reads entries produced recursively using shared attention. We address this mismatch by aligning the two training passes' cache entries with a consistency loss during the final 10% of a fixed training-token budget. Across three model widths on FineWeb, this removes 81--89% of the continuation-loss penalty relative to full-cache evaluation. The improvement replicates on disjoint documents for two training seeds. Across four widths and 1.05--4.19B training tokens, the loss cost of weight tying is smallest at lower token budgets, suggesting an opportunity to use recurrent computation when data and model storage are limited.

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BibTeX RIS

Andreas Kirsch. 2026-10-23. LSTM-Style Looped Transformers with Shared KV Caches. https://arxiv.org/abs/2610.33360

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