AVSD: Adaptive-View Self-Distillation by Balancing Consensus and Teacher-Specific Privileged Signals
arXiv:2605.20643v1 Announce Type: new Abstract: Self-distillation enables language models to learn on-policy from their own trajectories by using the same model as both student and teacher, with the teacher being conditioned on privileged information unavailable to the student. Such information can come in different types or views, such as solutions, demonstrations, feedback, or final answers. This setup provides dense token-level feedback without relying on a separate external model, but creates a fundamental asymmetry: the teacher may rely on view-specific information that the student cannot
This research addresses a fundamental challenge in self-distillation for large language models, a technique gaining prominence for efficient training and performance improvement.
Improving self-distillation methods can lead to more robust and capable language models without needing extensive external data or complex multi-model setups, impacting AI development efficiency.
This advancement potentially allows language models to learn more effectively from their own internally generated feedback, reducing reliance on human labeling or distinct teacher models.
- · AI researchers and developers
- · Companies using or building large language models
- · AI platform providers
This method could lead to more efficient and sophisticated language model training paradigms.
The ability to leverage 'privileged information' within self-distillation might accelerate the development of more autonomous and adaptable AI systems.
Improved self-learning capabilities in models could reduce the human effort required for model refinement, potentially accelerating the development timelines for complex AI applications.
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Read at arXiv cs.LG