[Paper] MoE-DiffuSeq: Enhancing Long-Document Diffusion Models with Sparse Attention and Mixture of Experts

Source: arXiv - 2512.20604v1

Overview

The paper introduces MoE‑DiffuSeq, a new framework that combines mixture‑of‑experts (MoE) routing with a custom sparse‑attention mechanism to make diffusion‑based text generation viable for very long documents. By tackling the notorious memory and compute bottlenecks of existing diffusion models (e.g., DiffuSeq), the authors push the technology closer to real‑world use cases such as scientific article drafting, code‑base synthesis, and multi‑turn dialogue bots.

Key Contributions

• Sparse‑attention diffusion backbone: A tailored attention scheme that scales roughly linearly with sequence length, dramatically cutting GPU memory usage.
• Mixture‑of‑Experts routing: Dynamically activates only a small subset of expert sub‑networks per token, further reducing FLOPs while preserving model capacity.
• Soft absorbing state: Integrated into the diffusion denoising steps to speed up convergence and improve token‑level reconstruction fidelity.
• Comprehensive benchmarking: Empirical results on long‑form datasets (scientific abstracts, code repositories, dialogue logs) showing up to 2–3× faster training/sampling and measurable gains in BLEU, ROUGE, and human‑rated coherence.
• Open‑source implementation: The authors release code and pretrained checkpoints, lowering the barrier for developers to experiment with diffusion‑based generation.

Methodology

1. Base diffusion model – Starts from DiffuSeq, which treats text generation as a reverse diffusion process: a noisy token sequence is gradually denoised back into readable text.
2. Sparse attention layer – Instead of the classic full‑self‑attention (O(N²) cost), the model computes attention only over a sliding window plus a set of learned “global” tokens. This reduces the per‑layer complexity to O(N·k) where k ≪ N.
3. Mixture‑of‑Experts (MoE) routing – Each transformer block contains multiple expert feed‑forward networks. A lightweight gating network selects the top‑k experts for each token, activating only those experts during forward/backward passes. This yields a high‑capacity model without proportional compute growth.
4. Soft absorbing state – During the diffusion steps, a small probability mass is allowed to “absorb” into a stable state, effectively shortening the number of diffusion timesteps needed for convergence.
5. Training & sampling – The model is trained with the standard variational diffusion loss, but with the added MoE regularization (load balancing loss) and sparse‑attention masks. Sampling follows the usual reverse diffusion schedule, now accelerated by the absorbing state.

Results & Findings

• Efficiency: Training speedup of ~2.5× and sampling latency cut by >50 % on 2k‑token sequences.
• Quality: Consistent improvements across automatic metrics and human evaluations, especially in maintaining global coherence over long spans.
• Scalability: Memory footprint grew only modestly when scaling from 1 B to 4 B parameters, thanks to MoE sparsity.

Practical Implications

• Developer tooling: IDE plugins that auto‑generate extensive documentation or code snippets can now rely on diffusion models without prohibitive latency.
• Content platforms: Newsrooms and scientific publishers could use MoE‑DiffuSeq to draft long articles, getting rapid first drafts that preserve structure.
• Conversational AI: Customer‑support bots handling multi‑turn, context‑rich dialogues can maintain coherence over hundreds of turns without exploding GPU costs.
• Edge‑friendly deployment: Because only a fraction of experts fire per token, inference can be sharded across multiple GPUs or even specialized accelerator clusters, making large‑scale generation more cost‑effective.
• Open‑source ecosystem: The released code integrates with Hugging Face Transformers, allowing developers to plug MoE‑DiffuSeq into existing pipelines with minimal friction.

Limitations & Future Work

• Expert imbalance: Despite load‑balancing losses, some experts can become under‑utilized, especially on highly homogeneous corpora.
• Sparse‑attention hyper‑tuning: Choosing the right window size and number of global tokens still requires dataset‑specific experimentation.
• Diffusion step count: While the soft absorbing state reduces steps, the model still needs dozens of reverse diffusion iterations, which may be a hurdle for ultra‑low‑latency applications.
• Future directions proposed by the authors include:
  1. Adaptive gating that learns to vary the number of active experts per token.
  2. Integrating retrieval‑augmented generation to further boost factual accuracy.
  3. Exploring hybrid autoregressive‑diffusion schedules to cut inference steps even more.

Authors

• Alexandros Christoforos
• Chadbourne Davis

Paper Information

• arXiv ID: 2512.20604v1
• Categories: cs.CL
• Published: December 23, 2025
• PDF: Download PDF