SPA-Cache: Singular Proxies for Adaptive Caching in Diffusion Language Models
📰 ArXiv cs.AI
Learn how SPA-Cache optimizes Diffusion Language Models with adaptive caching, reducing costly hidden state recomputation
Action Steps
- Implement SPA-Cache in your DLM architecture to enable adaptive caching
- Replace traditional KV caching with singular proxies for more efficient hidden state updates
- Configure the caching budget to balance computational costs and model performance
- Test the impact of SPA-Cache on your model's decoding speed and accuracy
- Compare the results with existing caching approaches to evaluate the effectiveness of SPA-Cache
Who Needs to Know This
NLP engineers and researchers working with Diffusion Language Models can benefit from this technique to improve model efficiency and reduce computational costs
Key Insight
💡 SPA-Cache enables efficient caching in Diffusion Language Models by using singular proxies and adaptive budgeting
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🚀 Improve DLM efficiency with SPA-Cache! Adaptive caching reduces hidden state recomputation costs 🤖
Key Takeaways
Learn how SPA-Cache optimizes Diffusion Language Models with adaptive caching, reducing costly hidden state recomputation
Full Article
Title: SPA-Cache: Singular Proxies for Adaptive Caching in Diffusion Language Models
Abstract:
arXiv:2602.02544v2 Announce Type: replace-cross Abstract: While Diffusion Language Models (DLMs) offer a flexible, arbitrary-order alternative to the autoregressive paradigm, their non-causal nature precludes standard KV caching, forcing costly hidden state recomputation at every decoding step. Existing DLM caching approaches reduce this cost by selective hidden state updates; however, they are still limited by (i) costly token-wise update identification heuristics and (ii) rigid, uniform budget
Abstract:
arXiv:2602.02544v2 Announce Type: replace-cross Abstract: While Diffusion Language Models (DLMs) offer a flexible, arbitrary-order alternative to the autoregressive paradigm, their non-causal nature precludes standard KV caching, forcing costly hidden state recomputation at every decoding step. Existing DLM caching approaches reduce this cost by selective hidden state updates; however, they are still limited by (i) costly token-wise update identification heuristics and (ii) rigid, uniform budget
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