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This research introduces DominoTree, an optimized tree-based drafting method that enhances speculative decoding for large language models by incorporating path-dependent causal corrections via a GRU mechanism. The approach achieves superior token acceptance rates and throughput across various benchmarks compared to existing methods like DFlash and EAGLE-3 without requiring additional training.
DominoTree: Conditional Tree-Structured Drafting with Domino for Speculative Decoding
arXiv:2607.08642v3 Announce Type: replace Abstract: Speculative decoding accelerates LLM inference by drafting tokens and verifying them in parallel. Block-diffusion drafters such as DFlash model only per-position marginals, and tree methods such as DDTree expand candidate trees from those marginals. The released Domino drafter adds a GRU-based causal correction making each draft token's distribution path-dependent, a structure DDTree's factorized formulation cannot represent. We introduce DominoTree, a training-free best-first draft tree scored by Domino's conditional (non-factorized) correction along each root-to-node path, made practical by restricting the per-node correction to a candidate top-M. We evaluate it on eight benchmarks in a single-stream harness, and in SGLang, where it runs as an out-of-tree plugin against AR, DFlash, EAGLE-3 and Domino under identical flags. DominoTree attains the highest mean accepted length in every serving cell - two model sizes, single-request and concurrent load, context to 32K - and the highest Overall accepted length at every temperature in the research harness (21 of 24 per-dataset cells). A three-arm decomposition holding drafter, budget and verifier fixed separates the gain from applying the correction at all (+10.1% accepted length) from that of recomputing it along each candidate's realized path (+4.7% more), the part this paper adds. Where the round is verify-dominated, throughput follows: up to 7.3x over AR on Qwen3-8B, beating the released Domino decoder at its CUDA-graph best at every temperature, and inside SGLang winning single-request throughput by +12% over Domino on Qwen3-8B. On HELMET long context it beats Domino by +29-36% accepted length and +10-34% throughput at every length and both model sizes. Past a memory-constrained card's admission cap the chain wins goodput, and at our longest context, where prefill dominates, our lead over EAGLE-3 narrows to a tie.
Abstract:Speculative decoding accelerates LLM inference by drafting tokens and verifying them in parallel. Block-diffusion drafters such as DFlash model only per-position marginals, and tree methods such as DDTree expand candidate trees from those marginals. The released Domino drafter adds a GRU-based causal correction making each draft token's distribution path-dependent, a structure DDTree's factorized formulation cannot represent. We introduce DominoTree, a training-free best-first draft tree scored by Domino's conditional (non-factorized) correction along each root-to-node path, made practical by restricting the per-node correction to a candidate top-M. We evaluate it on eight benchmarks in a single-stream harness, and in SGLang, where it runs as an out-of-tree plugin against AR, DFlash, EAGLE-3 and Domino under identical flags. DominoTree attains the highest mean accepted length in every serving cell - two model sizes, single-request and concurrent load, context to 32K - and the highest Overall accepted length at every temperature in the research harness (21 of 24 per-dataset cells). A three-arm decomposition holding drafter, budget and verifier fixed separates the gain from applying the correction at all (+10.1% accepted length) from that of recomputing it along each candidate's realized path (+4.7% more), the part this paper adds. Where the round is verify-dominated, throughput follows: up to 7.3x over AR on Qwen3-8B, beating the released Domino decoder at its CUDA-graph best at every temperature, and inside SGLang winning single-request throughput by +12% over Domino on Qwen3-8B. On HELMET long context it beats Domino by +29-36% accepted length and +10-34% throughput at every length and both model sizes. Past a memory-constrained card's admission cap the chain wins goodput, and at our longest context, where prefill dominates, our lead over EAGLE-3 narrows to a tie.
32 pages, 2 figures, 16 tables. Code: this https URL
Computation and Language (cs.CL)
arXiv:2607.08642 [cs.CL]
(or arXiv:2607.08642v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.08642 Focus to learn more arXiv-issued DOI via DataCite
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