arXiv:2602.12262v4 Announce Type: replace-cross Abstract: Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this potential in practice remains challenging: reducing the number of decoding steps, typically causes a substantial degradation in output quality due to token factorization error. To alleviate this, we propose a self-distillation framework that trains a few-step student to match the generative trajectory of a full-step teacher. We theoretically and empirically show th
The continuous drive for faster and more efficient AI models is pushing researchers to address fundamental bottlenecks in generative AI, such as the slow inference of diffusion models.
Improving the inference speed of large language models while maintaining output quality could significantly reduce compute costs and unlock new applications for real-time AI generation.
The potential to make diffusion LLMs significantly faster and more practical for deployment, especially in contexts requiring rapid response or high throughput.
- · AI developers
- · Cloud providers
- · AI-powered applications
- · Generative AI users
- · Competitors using slower generative models
- · Organizations with high latency requirements
Reduced computational overhead for deploying large language models.
Expansion of real-time AI capabilities across various industries, from customer service to content creation.
Increased competition and innovation in the AI model optimization space, accelerating the commoditization of generative AI.
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Read at arXiv cs.LG