arXiv (All)
open access
Acceptance-Aware Draft Model Training for Speculative Decoding
Speculative decoding accelerates large language model (LLM) inference by using a lightweight draft model to generate multiple candidate tokens that are verified by the target model in a single forward pass. Its speedup is largely determined by the acceptance length, yet existing draft-model training
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
- To Copy or Not to Copy: Controlling Speculative Decoding via Intrinsic Model Signals#1033956
- Mentored Decoding: Faster Inference meets Boosting#1104673
- Osprey: Target-agnostic Pre-training Makes Stronger Drafters in Speculative Decoding#987097
- Forgetting Only What Matters: Layer-Selective Unlearning toward Robust LLMs#997322
- Dataset Watermarking with Provable Black-Box Detection#1130546
Record · ID 1050335
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