arXiv (All)
open access
Divergence-Routed On-Policy Distillation for Language Models
On-policy distillation (OPD) transfers knowledge from a teacher model to a student along student-generated trajectories, typically by minimizing the reverse Kullback-Leibler (RKL) divergence. However, RKL can provide weak corrective signals for teacher-supported tokens to which the student assigns v
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
- CompassOPD: Cross-Family On-Policy Distillation via Within-Family Likelihood Shifts#997065
- Dense Is Not Enough: Hierarchical Supervision Allocation for Long-Horizon On-Policy Distillation#1118121
- MOPD-Router: Rethinking Teacher Routing in Multi-Teacher On-Policy Distillation#1105396
- KDFP: A first-principles approach to knowledge distillation in large language models#1247781
- UP-MOPD: Update Projection in Multi-Teacher On-Policy Distillation#1218133
Record · ID 1354263
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