arXiv (All)
open access
Learning how to Forget: Fine-tuning for Long-Context Sparse Attention
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. It works for any KV cache policy
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Language Models Can Control Their Own Attention#924873
- Language Models Can Control Their Own Attention#920639
- IronLLM: Forging Compact Edge-Native Language Models for Real-Time Embodied Intelligence#1132061
- AgentIR: A Workload-Adaptive Cascade Retrieval Substrate for Long-Term Conversational Memory#229612
- Lost in the Flow with Code Talkers: Unveiling the Instruction-Tuning Tax of Large Language Models in Code Tasks#267720
Record · ID 1128636
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive