SimOPD: Relational Knowledge Transfer for On-Policy Distillation
In brief
- SimOPD transfers teacher internal knowledge via response similarities.
- Uses memory banks to extract supervision distributions without cross-model projection.
- Boosts performance by 2.6 to 3.1 points over existing methods.
SimOPD introduces a new approach to on-policy distillation by transferring teacher internal knowledge through response similarities. It leverages memory banks to extract supervision distributions, enabling better performance without cross-architecture constraints.
This summary gives you the essentials. The full article, its figures and quotes are on cs.AI updates on arXiv.org.
Read the original articleAI-generated summary of the source, checked by rules. Report an error