SpecLA: Efficient Speculative Decoding for Linear-Attention Models

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[Submitted on 18 Jul 2026]

Title:SpecLA: Efficient Speculative Decoding for Linear-Attention Models

View a PDF of the paper titled SpecLA: Efficient Speculative Decoding for Linear-Attention Models, by Zhibin Wang and 7 other authors

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Abstract:Linear-attention models replace the growing KV cache with recurrent states, but autoregressive decoding still reads, updates, and writes these states one token at a time. Speculative decoding can reduce this cost by verifying several draft tokens in one target pass, yet existing speculative systems are designed for Transformer KV caches. For stateful linear-attention targets, verification must follow recurrent dependencies across chains and branches, acceptance must update only the accepted state trajectory, and the drafter must avoid submitting candidates that waste stateful verification work. This paper presents SpecLA, a speculative decoding runtime for stateful linear-attention models. SpecLA verifies chains and trees with topology-aware kernels, stores compact factors produced during verification to recover accepted states, and uses confidence pruning plus a target-aligned EAGLE-style drafter to feed useful candidates to the verifier. On an NVIDIA H100 with a public GDN-1.3B target, SpecLA achieves up to 1.70x end-to-end speedup over autoregressive decoding.

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2607.16673 [cs.CL]

(or arXiv:2607.16673v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.16673

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Fuliang Liu [view email] [v1] Sat, 18 Jul 2026 07:08:19 UTC (6,576 KB)

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