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arXiv · 2605.18807

Block-Based Double Decoders

Asher Labovich·Benjamin Bradley·Vanessa Alexander·Chaitanya Harsha
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Abstract

Encoder-decoder models offer substantial inference-time savings over decoder-only models, but their pretraining objectives suffer from sparse supervision and dynamic sequence lengths, keeping them out of practice at scale. We propose block-based double decoders, a novel transformer architecture that utilizes doubly-causal block-based attention masks to train with full loss supervision and static sequence packing, combining decoder-only training efficiency with encoder-decoder inference efficiency. In scaling law experiments, block-based double decoders strongly outperform encoder-decoders and closely track decoder-only models across scales. At inference time, they cut KV-cache memory and per-token compute by at least 2/3 without sacrificing prefill caching or other existing inference optimizations available to decoder-only models.

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Asher Labovich, Benjamin Bradley, Vanessa Alexander, Chaitanya Harsha. 2026-05-11. Block-Based Double Decoders. https://arxiv.org/abs/2605.18807

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