Prefix Teach, Suffix Fade: Local Teachability Collapse in Strong-to-Weak On-Policy Distillation
arXiv:2605.13643v2 Announce Type: replace Abstract: On-policy distillation (OPD) trains a student model on its own rollouts using dense feedback from a stronger teacher. Prior literature suggests that, provided teacher feedback is available, supervising the full sequence of response tokens should monotonically improve performance. However, we demonstrate that this assumption sometimes fails to hold in strong-to-weak OPD settings. While later segments of a generated trajectory may still exhibit a non-zero teacher-student advantage, they frequently lack the local contrast that makes dense feedba
This research emerges as AI model distillation and efficiency become critical for practical deployment and resource optimization.
Understanding the limitations of existing distillation techniques is crucial for improving AI training efficiency and model performance, affecting various AI applications.
The assumption that dense feedback monotonically improves on-policy distillation is now challenged, suggesting more nuanced approaches are needed for optimal results.
- · Researchers specializing in advanced AI training techniques
- · AI developers using multi-model systems
- · Developers relying on simplistic distillation methods
- · AI projects with high compute costs due to inefficient training
Refinement of distillation algorithms will be necessary to address 'local teachability collapse'.
New architectures or training paradigms might emerge to optimize student model learning in strong-to-weak teacher scenarios.
More efficient and performant AI models could accelerate the deployment of complex AI systems across industries.
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Read at arXiv cs.CL