arXiv (All)
open access
Graph-Conditioned On-Policy Agent Distillation from Off-the-Shelf Teachers
On-policy distillation (OPD) trains compact language agents with teacher feedback on student-generated trajectories. In multi-turn tasks, compounding errors can move students beyond the teacher's effective supervision. We introduce Graph-Conditioned On-Policy Agent Distillation (GC-OPD), which enric
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
- Beyond Token Alignment: Event Completion for Cross-Tokenizer On-Policy Distillation#1128996
- Interactive-Policy Distillation with Bidirectional Propose-and-Verify#1131747
- OVD: On-policy Verbal Distillation#1128551
- Reward-Aligned Reweighting for On-Policy Distillation#1130098
Record · ID 1141843
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