$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction
📰 ArXiv cs.AI
Accelerate diffusion large language models by reducing spatio-temporal redundancy, improving inference latency
Action Steps
- Analyze the decoding process of diffusion large language models to identify spatial and temporal redundancy
- Apply spatio-temporal redundancy reduction techniques to minimize recurring redundancy
- Implement $R^2$-dLLM to accelerate diffusion large language models
- Evaluate the performance of $R^2$-dLLM using metrics such as inference latency and accuracy
- Compare the results with existing methods to demonstrate the effectiveness of $R^2$-dLLM
Who Needs to Know This
NLP engineers and researchers can benefit from this technique to improve the efficiency of their language models, enabling faster deployment and better performance
Key Insight
💡 Reducing spatio-temporal redundancy in diffusion large language models can significantly improve inference latency
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🚀 Accelerate your diffusion large language models with $R^2$-dLLM! 📚
Key Takeaways
Accelerate diffusion large language models by reducing spatio-temporal redundancy, improving inference latency
Full Article
Title: $R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction
Abstract:
arXiv:2604.18995v1 Announce Type: cross 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
Abstract:
arXiv:2604.18995v1 Announce Type: cross 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
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