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ReWY: Faster Gradient Computation for Delta-Rule Attention Family

Paper recorded by Signals 4 on 2026-11-01 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-01 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.LG · 机器学习 · first seen 2026-11-03

Abstract

Delta-rule linear attention underlies language models such as Kimi Linear and Qwen3-Next. Chunkwise implementations expose matrix parallelism, but their backward passes compute unnecessary auxiliary adjoints. We present ReWY, an algebraic reformulation that reduces this work before optimizing accelerator execution. Differentiating the chunk correction as one triangular residual system eliminates t

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#34 most recent of 703 cs.LG papers we have recorded · ↑ newer: Direct Knowledge Transfer for Offline Q-Learning · ↓ older: Watch Your Step: Adam's Margin Maximization Bias Depends on the Learni
Cite this page: ReWY: Faster Gradient Computation for Delta-Rule Attention Family: the #34 most recent of 703 cs.LG papers we have recorded (as of 2026-11-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/rewy-faster-gradient-computation-for-delta-rule-attention-family.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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