arXiv (All)
open access
Do As I Do, Not As I Say: On-Policy Distillation Inherits the Teacher in Vision-Language Models
Improving the reasoning capabilities of vision-language models (VLMs) often relies on post-training with supervision from stronger models. Two common strategies are supervised fine-tuning (SFT), which trains on teacher-generated demonstrations, and on-policy distillation (OPD), which provides teache
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
- DASH-OPD: Discrepancy-Aware Switching with Hysteresis for On-Policy Distillation#1070478
- On-Policy Visual Evidence Distillation#1132039
- ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning#1118911
- OPD-Aha: From Linguistic Momentum to Visual Reflection in Multimodal On-Policy Distillation#1122504
- Geometric Self-Distillation for Reasoning Generalization#1202138
Record · ID 1356325
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