arXiv (All)
open access
Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective
On-policy distillation (OPD) has become an important approach to language model post-training. However, despite its performance gains, OPD can also collapse into excessively long and repetitive generation, and the mechanism underlying these divergent outcomes remains poorly understood. We explain th
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
- Learning from Language Feedback via Variational Policy Distillation#187307
- Rethinking On-Policy Distillation of Large Language Models II: One Training Example#927037
- Contrastive On-Policy Distillation#1143849
- Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement#819076
- Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement#809084
Record · ID 1187863
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