arXiv (OAI Expanded)
open access
Influence-Directed Distillation: Solving the Diversity Bottleneck in Sampled-Token On-Policy Distillation
Sampled-token on-policy distillation (OPD) efficiently transfers capabilities from teacher to student using student-generated tokens, requiring teacher probabilities only for sampled tokens. Yet it frequently suffers from diversity distillation failure: the student's pass@1 improves while its pass@$
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
- Influence-Directed Distillation: Solving the Diversity Bottleneck in Sampled-Token On-Policy Distillation#798469
- Leaky Students: Membership Inference against On-Policy Distillation#1117848
- No Pain, More Gain: Iterative Merging for Effective Multi-Teacher On-Policy Distillation#1129003
- 1% of Tokens Can Be Enough: On Gradient Estimation in On-Policy Distillation#1050609
- Rethinking On-Policy Distillation of Large Language Models II: One Training Example#927037
Record · ID 797056
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