arXiv (All)
open access
On-Policy Attention Linearization
Hybrid transformer architectures that replace most softmax attention layers with linear attention offer transformer-level quality at a fraction of the memory cost. Rather than pretraining such models, a growing body of work distills them from already trained full-attention transformers. However, the
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
- A Unified Perspective on the Dynamics of Deep Transformers#1120649
- Quasi Linear Kernel Attention with Infinite Capacity#1129941
- Approximating Softmax in Pretrained LLMs: Model Sensitivity and Kernel Acceleration#1118298
- Sequential Functional Structured Tucker Compression for Large Language Model Attentions#1155652
- SMat-Attention: Structured Long-Context Sequence Modeling#1130904
Record · ID 1115903
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