Scaling Beyond Masked Diffusion Language Models (Feb 2026)

Описание: Title: Scaling Beyond Masked Diffusion Language Models (Feb 2026)
Link: http://arxiv.org/abs/2602.15014v1
Date: February 2026

Summary:
This paper presents the first scaling law study for uniform-state (Duo) and interpolating (Eso-LM) discrete diffusion language models. It challenges the assumption that Masked Diffusion is the definitive future of diffusion language modeling by showing that although it has superior likelihood scaling, other diffusion families offer better speed-quality Pareto frontiers due to more efficient sampling. The study scales models up to 1.7B parameters, finding that uniform-state diffusion (Duo) outperforms both autoregressive and masked diffusion models on reasoning benchmarks like GSM8K.

Key Topics:
Discrete Diffusion Language Models
Scaling Laws
Masked Diffusion (MDLM)
Uniform-state Diffusion (Duo)
Speed-Quality Pareto Frontier
IsoFLOP Analysis
Low-variance Training Objectives

Chapters:
00:00 - Avoiding the Metric Trap
01:24 - Defining Modern Model Architectures
02:49 - Breaking Down Diffusion Families
04:46 - Analyzing the Compute Tax
06:18 - Optimizing Training Loss Variance
07:51 - Quality vs Throughput Frontier
09:11 - Duo Model Speed Benefits
10:49 - Scaling Up Parameters Performance
12:22 - Disruptive Math Reasoning Results
14:13 - Explaining Global Self-Correction
15:53 - Specialized Architectural Tradeoffs
17:24 - Navigating Information Globally
19:08 - Actionable Engineering Takeaways
20:26 - Future Diffusion Optimization Potential

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