Component-Aware Self-Speculative Decoding in Hybrid Language Models
Speculative decoding accelerates autoregressive inference by drafting candidate tokens with a fast model and verifying them in parallel with the target. Self-speculative methods avoid the need for an external drafter but have been studied exclusively in homogeneous Transformer architectures. We introduce component-aware self-speculative decoding, the first method to exploit the internal architectural heterogeneity of hybrid language models, isolating the SSM/linear-attention subgraph as a zero-cost internal draft. We evaluate this on two architecturally distinct hybrid families: Falcon-H1 (parallel: Mamba-2 + attention per layer) and Qwen3.5 (sequential: interleaved linear and attention l...
| Authors | Hector Borobia, Elies Seguí-Mas, Guillermina Tormo-Carbó |
|---|---|
| Primary category | cs.CL |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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