arXiv (All)
open access
Latent-MOPD: Latent Multi-Teacher On-Policy Distillation
On-policy distillation (OPD) trains a student on the responses it generates. Existing LLM multi-teacher OPD transfers what specialists predict through their output distributions. We introduce Latent-MOPD, to our knowledge the first representation-level multi-teacher OPD method for LLMs. It integrate
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
- Contrastive On-Policy Distillation#1143849
- Distill What You Trust: Reliability-Aware Multi-Teacher On-Policy Distillation#1049433
- Data-free On-policy Distillation#1136554
- Radioactive Feedback: Tracing Models Trained by Label-Only LLM Supervision#1326935
- H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-policy Distillation#1297188
Record · ID 1186695
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