# Accelerating Speculative Decoding with Block Diffusion Draft Trees

arXiv: https://arxiv.org/abs/2604.12989 · HTML: https://mlsanity.com/paper/2604.12989/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Speculative decoding, one of 12 directions in Efficient LLM inference and serving.
- Builds on 23 of its 27 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Placed in

- Efficient LLM inference and serving / [Speculative decoding](https://mlsanity.com/inference/speculative-decoding/)

## Builds on (most cited first, 23 of 23)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [Accelerating Large Language Model Decoding with Speculative Sampling](https://mlsanity.com/paper/2302.01318/) (2023)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty](https://mlsanity.com/paper/2401.15077/) (2024)
- [Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models](https://mlsanity.com/paper/2503.09573/) (2025)
- [EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees](https://mlsanity.com/paper/2406.16858/) (2024)
- [SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification](https://mlsanity.com/paper/2305.09781/) (2023)
- [EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test](https://mlsanity.com/paper/2503.01840/) (2025)
- [Accelerating LLM Inference with Staged Speculative Decoding](https://mlsanity.com/paper/2308.04623/) (2023)
- [DFlash: Block Diffusion for Flash Speculative Decoding](https://mlsanity.com/paper/2602.06036/) (2026)
- [OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure](https://mlsanity.com/paper/2406.17276/) (2024)
- [PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation](https://mlsanity.com/paper/2504.18583/) (2025)
- [Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement](https://mlsanity.com/paper/2402.14160/) (2024)
- [DySpec: Faster Speculative Decoding with Dynamic Token Tree Structure](https://mlsanity.com/paper/2410.11744/) (2024)
- [DART: Diffusion-Inspired Speculative Decoding for Fast LLM Inference](https://mlsanity.com/paper/2601.19278/) (2026)

## Built on by (most cited first, 29 of 29)

- [JetSpec: Breaking the Scaling Ceiling of Speculative Decoding with Parallel Tree Drafting](https://mlsanity.com/paper/2606.18394/) (2026)
- [xPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding](https://mlsanity.com/paper/2608.02438/) (2026)
- [DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees](https://mlsanity.com/paper/2608.13524/) (2026)
- [TreeFlash: Parallel AR-Approximation for Faster Speculative Decoding](https://mlsanity.com/paper/2606.03819/) (2026)
- [DeLS-Spec: Decoupled Long-Short Contexts for Parallel Speculative Drafting](https://mlsanity.com/paper/2607.07409/) (2026)
- [LiLiCorr: Lightweight Likelihood Correlation of Parallel Drafts for Speculative Decoding](https://mlsanity.com/paper/2608.20530/) (2026)
- [Teaching Diffusion to Speculate Left-to-Right](https://mlsanity.com/paper/2606.11552/) (2026)
- [Trees from Marginals: Autoregressive drafting with factorized priors](https://mlsanity.com/paper/2607.06763/) (2026)
- [From Chains to Trees: Parent-Conditioned Drafting for Semi-Autoregressive Speculative Decoding](https://mlsanity.com/paper/2608.02123/) (2026)
- [Bastion: Budget-Aware Speculative Decoding with Tree-structured Block Diffusion Drafting](https://mlsanity.com/paper/2605.29727/) (2026)
- [D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models](https://mlsanity.com/paper/2606.04446/) (2026)
- [DominoTree: Conditional Tree-Structured Drafting with Domino for Speculative Decoding](https://mlsanity.com/paper/2607.08642/) (2026)
- [Margins, Not Windows: Training-Free Per-Step Lossy Speculative Decoding](https://mlsanity.com/paper/2609.02897/) (2026)
- [Carryover Drafting: Recycling Rejected States for Speculative Decoding](https://mlsanity.com/paper/2609.14717/) (2026)
- [TreeSpark: Calibrated, Load-Adaptive Draft Trees for Semi-Autoregressive Speculative Decoding](https://mlsanity.com/paper/2609.22098/) (2026)
- [Draft in Parallel, Condition Through Depth: Adjacent Causal Injection for Speculative Decoding](https://mlsanity.com/paper/2609.36173/) (2026)
- [Mean-Field Parallel Decoding for Discrete Diffusion Language Models](https://mlsanity.com/paper/2606.15805/) (2026)
- [Fully Rotation-Equivariant Spectral-Spatial Learning for Multispectral Object Detection](https://mlsanity.com/paper/2607.05148/) (2026)
- [LibraSpec: Dynamic Diffusion-Based Speculative Decoding via Marginal-Gain-Driven Optimization](https://mlsanity.com/paper/2608.08721/) (2026)
- [Is Multimodal Speculative Decoding Ready for Diffusion-Based Parallel Drafting? A Survey and Empirical Diagnosis](https://mlsanity.com/paper/2608.20743/) (2026)
- [Ceiling-Clipped Acceptance Histograms Indicate Stranded Speed-up in Block-Diffusion Speculative Decoding](https://mlsanity.com/paper/2608.30427/) (2026)
- [Vision Is Not Overhead: One-Pass Block Drafting for Lossless Speculative Decoding in Vision-Language Models](https://mlsanity.com/paper/2609.00355/) (2026)
- [Tsubame: Tree Replay for Diffusion-Based Speculative Decoding](https://mlsanity.com/paper/2609.33652/) (2026)
- [LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter](https://mlsanity.com/paper/2609.37029/) (2026)
- [UBTree: Parallel Tree Drafting via Unigram and Bigram Models for Speculative Decoding](https://mlsanity.com/paper/2609.39972/) (2026)
- [AdaSpark: Adaptive DSpark with Online Learning for Tree Verification and N-gram Fill](https://mlsanity.com/paper/2610.05774/) (2026)
- [DLoop: Looped Speculative Decoding](https://mlsanity.com/paper/2610.07659/) (2026)
- [Where Draft Trees Lose Target Mass: Exit-Guided Speculative Decoding](https://mlsanity.com/paper/2610.11750/) (2026)
- [TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding](https://mlsanity.com/paper/2610.20175/) (2026)

## Abstract (arXiv)

Speculative decoding accelerates autoregressive language models by using a lightweight drafter to propose multiple future tokens, which the target model then verifies in parallel. DFlash shows that a block diffusion drafter can generate an entire draft block in a single forward pass and achieve state-of-the-art speculative decoding performance, outperforming strong autoregressive drafters such as EAGLE-3. Vanilla DFlash, however, still verifies only a single drafted trajectory per round, potentially limiting its acceptance length. We introduce DDTree (Diffusion Draft Tree), a method that constructs a draft tree directly from the per-position distributions of a block diffusion drafter. Under a fixed node budget, DDTree uses a simple best-first heap algorithm to select the continuations that are most likely to match the target model according to a surrogate defined by the draft model's output. The resulting tree is verified efficiently in a single target model forward pass using an ancestor-only attention mask. Because DDTree builds on DFlash, a leading draft model for speculative decoding, these gains place DDTree among the leading approaches to speculative decoding.