arXiv (All)
open access
Commit Locally, Exit Globally: Coordinating Adaptive Sampling and Early Exit in Diffusion Language Models
Diffusion language models predict tokens in parallel but still require many iterative forward passes. Adaptive sampling accelerates local token commitment, while recent global stopping methods seek to end generation early. Our trajectory analysis shows that short answers can stabilize early in decod
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
- 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
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#797321
- Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models#800070
Record · ID 1344663
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