Infini-Attention Boosts Long-Context Performance in Small Language Models
Published:Dec 29, 2025 21:02
•1 min read
•ArXiv
Analysis
This paper explores the use of Infini-attention in small language models (SLMs) to improve their ability to handle long-context inputs. This is important because SLMs are more accessible and cost-effective than larger models, but often struggle with long sequences. The study provides empirical evidence that Infini-attention can significantly improve long-context retrieval accuracy in SLMs, even with limited parameters. The identification of the balance factor and the analysis of memory compression are valuable contributions to understanding the limitations and potential of this approach.
Key Takeaways
Reference
“The Infini-attention model achieves up to 31% higher accuracy than the baseline at a 16,384-token context.”