arXiv (All)
open access
H-OPD: Confidence Aware Heterogeneous Multi-Teacher Multimodal On-policy Distillation
On-policy distillation (OPD) has recently emerged as an effective post-training paradigm by providing supervision on student-generated trajectories. However, existing OPD methods for multimodal reasoning usually rely on a static teacher routing, assigning each sample to a single teacher based on mod
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
- Is Better Teacher Supervision Enough? Unlocking Student-side Learning in Multimodal On-Policy Distillation#1144087
- MetaOPD: Meta-Learned Token Weighting for On-Policy Distillation#1249269
- Latent-MOPD: Latent Multi-Teacher On-Policy Distillation#1186695
- How Should Teachers Be Prepared? RL on Student-Induced States for On-Policy Distillation#1200601
- Dr. OPD: Learning What to Follow for Optimal On-Policy Distillation of Large Language Models#1142326
Record · ID 1297188
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