---
title: "Multi-token Residual Prediction"
url: https://daily.dev/posts/multi-token-residual-prediction-mnbtznk7u
source_url: https://modal.com/blog/multi-token-residual-prediction
type: article
source: "Modal"
published: 2026-07-01T15:30:26.861Z
updated: 2026-07-01T16:23:06.312Z
tags: ["ai-inference"]
reading_time: 14
upvotes: 1
comments: 1
language: en
---

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# Multi-token Residual Prediction

**[Modal](https://daily.dev/sources/modal_labs)** · 14 min read · 1 upvotes · 1 comments

## Summary

Multi-Token Residual Prediction (MRP) is a lightweight transformer module (3 layers) that accelerates diffusion language model (DLM) inference by predicting inter-step logit residuals rather than full distributions. Naive multi-token prediction collapses on DLMs beyond one step, but predicting the small correction between adjacent denoising steps is a low-complexity target a tiny module can handle. MRP serves two inference regimes: in static denoising it enables speculative decoding (up to 1.56× throughput in SGLang with lossless quality) or direct decoding (up to 1.9× with minor quality cost); in dynamic denoising it remasks over-eagerly revealed tokens using the residual signal, recovering up to +22.6 accuracy points on benchmarks like GSM8K, MATH500, HumanEval, and MBPP across SDAR 1.7B/4B/8B models. The module attaches to a frozen backbone, requires no backbone retraining, and composes with existing DLM inference methods.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://modal.com/blog/multi-token-residual-prediction>

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---

Tags: [#ai-inference](https://daily.dev/tags/ai-inference)

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