arXiv (All)
open access
No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation
Multi-teacher on-policy distillation (MOPD) combines independently developed domain teachers into a single student by distilling their predictions on student-generated samples. We study a setting where teachers share a reference model but undergo different post-training procedures, and find that MOP
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
- Distillation as Probability Transport: Routed On-Policy Distillation#985698
- Trapped by Their Own Rollouts: Understanding Aggregation--Rollout Feedback in Federated On-Policy Distillation#1116904
- REVO: Rollout-Efficient Off-Policy Distillation via Variance-Guided Reuse#1141822
- Verify Before You Distill: Prompt-Level Teacher Gating for On-Policy Distillation#925371
Record · ID 1129003
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