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Learning how to Forget: Fine-tuning for Long-Context Sparse Attention

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A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer language models without excessive hardware budgets. We provide a new method for fine-tuning models with sparse attention.

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arXiv Computation and Language

23h ago

Learning how to Forget: Fine-tuning for Long-Context Sparse Attention

arXiv:2608. 19920v1 Announce Type: new Abstract: A lot of prior work addressed key-value (KV) cache selection and compression by sparse attention to enable long-context inference for transformer language models without excessive hardware budgets.

By Matthias Seeger, Zeyu Zhang, Vihang Patil, Konstantinos Benidis, Sebastian Schelter

llms fine-tuning efficiency

arXiv Machine Learning

Jul 1

RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference

arXiv:2606. 31519v1 Announce Type: new Abstract: Long-context Large Language Model inference is severely bottlenecked by the massive Key-Value (KV) cache, yet existing sparse attention methods often suffer from static fixed-budget (Top-k) retrieval or rely on proxy scores that are computationally expensive and biased.

By Wenhao Li, Jinhao Dong, Hailin Zhang, Wenhang Shi, Wei Lu, Xiaoyong Du

llms efficiency benchmarks

arXiv Machine Learning

Jul 2

FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel

arXiv:2508. 18224v3 Announce Type: replace-cross Abstract: Recent advances in sparse attention mechanisms have demonstrated strong potential for reducing the computational cost of long-context training and inference in large language models (LLMs).

By Ran Yan, Youhe Jiang, Zhuoming Chen, Haohui Mai, Beidi Chen, Binhang Yuan

llms benchmarks

arXiv AI

Jun 9

FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention

arXiv:2606. 09079v1 Announce Type: cross Abstract: Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra-long context serving.

By Yan Wang, Qifan Zhang, Jiachen Yu, Tian Liang, Dongyang Ma, Xiang Hu, Zibo Lin, Chunyang Li, Zhichao Wang, Jia Li, Yujiu Yang, Haitao Mi, Dong Yu

llms efficiency

arXiv AI

Jun 30

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers

arXiv:2606. 29844v1 Announce Type: cross Abstract: The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particularly in long-context scenarios.

By Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin Xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui

llms

arXiv Machine Learning

Jun 3

Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving

arXiv:2606. 02964v1 Announce Type: cross Abstract: Large Language Model (LLM) inference relies on key-value (KV) caches to avoid redundant attention computation.

By Chunan Shi, Yilei Chen, Yilin Chen, Xupeng Miao, Bin Cui

llms rag agents efficiency

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Related stories

Learning how to Forget: Fine-tuning for Long-Context Sparse Attention

RaBitQCache: Rotated Binary Quantization for KVCache in Long Context LLM Inference

FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel

FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers

Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving