arXiv (All)
open access
Adaptive Multi-Token Prediction: How far should we go?
Multi-token prediction (MTP) can accelerate language-model decoding by drafting several tokens and verifying them in a single target-model pass. However, the best speculation horizon changes with context: longer drafts offer greater amortization when reliable, but can waste compute on suffixes disca
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
- Adaptive Multi-Token Prediction: How far should we go?#1394224
- BitMTP: When Multi-Token Prediction Meets Low-Bit Large Language Models#1412560
- BitMTP: When Multi-Token Prediction Meets Low-Bit Large Language Models#1394207
- Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap#1342357
- Counting and Min-Cost Encoding for Tokenization in Large Language Models#1156062
Record · ID 1412676
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