# Approximate Speculative Decoding

arXiv: https://arxiv.org/abs/2608.03447 · HTML: https://mlsanity.com/paper/2608.03447/

## Key facts

- Cited by 1 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 13 of its 19 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Placed in

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

## Builds on (most cited first, 13 of 13)

- [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)
- [Blockwise Parallel Decoding for Deep Autoregressive Models](https://mlsanity.com/paper/1811.03115/) (2018)
- [OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure](https://mlsanity.com/paper/2406.17276/) (2024)
- [Ouroboros: Generating Longer Drafts Phrase by Phrase for Faster Speculative Decoding](https://mlsanity.com/paper/2402.13720/) (2024)
- [Fuzzy Speculative Decoding for a Tunable Accuracy-Runtime Tradeoff](https://mlsanity.com/paper/2502.20704/) (2025)
- [SpecPV: Improving Self-Speculative Decoding for Long-Context Generation via Partial Verification](https://mlsanity.com/paper/2512.02337/) (2025)
- [MARS: Unleashing the Power of Speculative Decoding via Margin-Aware Verification](https://mlsanity.com/paper/2601.15498/) (2026)
- [Cactus: Accelerating Auto-Regressive Decoding with Constrained Acceptance Speculative Sampling](https://mlsanity.com/paper/2604.04987/) (2026)
- [Accelerate Speculative Decoding with Sparse Computation in Verification](https://mlsanity.com/paper/2512.21911/) (2025)
- [Polybasic Speculative Decoding Through a Theoretical Perspective](https://mlsanity.com/paper/2510.26527/) (2025)

## Built on by (most cited first, 1 of 1)

- [Nucleus Speculative Decoding: Plausibility-Aware Verification Beyond Exact Distribution](https://mlsanity.com/paper/2610.07822/) (2026)

## Abstract (arXiv)

Speculative decoding accelerates autoregressive generation by verifying a draft block with a target model in parallel. Under standard greedy verification, decoding stops at the first draft token that differs from the target argmax, discarding the remaining target-scored suffix. Although accepting such a mismatch changes the decoding trajectory, it can make a contiguous suffix reusable when its tokens remain target-greedy under the realized prefix. In this paper, we introduce Approximate Speculative Decoding (ASD), a training-free verifier that replaces binary first-mismatch truncation with budgeted longest-prefix selection. ASD accepts selected mismatches subject to a local target-logit regret gate, a per-block exception cap, and a persistent request-level regret budget, then reuses the contiguous target-greedy suffix without additional approximate decisions or target-model forward passes. ASD requires neither a new draft model nor fine-tuning, and exactly reduces to standard greedy verification when the budget is zero. Experiments show that ASD improves fixed-workload throughput by 3.05%--15.26% over matched strict verification and averages a 7.78% gain across seven Qwen3-14B + DSpark-14B tasks. On DeepSeek-V4-Flash (284B) with DSpark it also raises verifier-side acceptance by roughly 10%--16% on GSM8K and MATH-500 in an FP4-to-FP8 compatibility setting. The source code is publicly available at: https://github.com/Kissmetothemoon/ASD