arXiv (All)
open access
Scaling and Distilling Text Embeddings for Better Diffusibility
Diffusion language models (DLMs) offer a promising alternative to autoregressive (AR) language generation. Recent advances in continuous DLMs, which apply latent diffusion to continuous text embeddings, raise a practical question: which embedding makes the best latent space, i.e., the most diffusibl
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
- Official Review · NEURIPS 2023#787218
- One Latent, Many Tokens: Jointly Learning Compressed Embeddings for Efficient Language Diffusion#1118771
- CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models#818956
- CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models#808964
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#798735
Record · ID 1155951
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