$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction
arXiv:2604.18995v2 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. However, practical dLLM decoding still suffers from high inference latency, which limits deployment. In this work, we observe that a substantial part of this inefficiency comes from recurring redundancy in the decoding process, including spatial redundancy caused by confidence clusters and positional ambiguity, and temporal redundancy caused by repeatedly remasking predictions that have already st
The paper addresses a core limitation of Diffusion Large Language Models (dLLMs) regarding high inference latency, a critical bottleneck for wider adoption and practical deployment, suggesting a timely solution.
Improving the efficiency of dLLMs by addressing spatial and temporal redundancy could significantly accelerate their development and deployment, making them a more viable alternative to current autoregressive models in real-world applications.
The proposed 'R^2-dLLM' method changes the performance ceiling for dLLMs, allowing for faster and potentially more cost-effective operation by reducing computational overhead from redundant processes.
- · AI compute providers
- · Developers of dLLMs
- · Cloud service providers
- · AI application developers
- · AI models reliant solely on autoregressive generation
- · Inefficient dLLM architectures
Faster dLLM inference leads to broader commercial applicability and reduced operational costs.
Increased adoption of dLLMs could shift the balance of power in foundational AI model development, competing more effectively with traditional large language models.
More efficient AI models could lessen the compute and energy demands per inference cycle, potentially impacting hardware innovation and sustainability efforts in AI.
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