arXiv:2606.11173v1 Announce Type: cross Abstract: Conditioning a language model on additional context, such as feedback on a previous attempt, typically improves its response. Self-distillation trains the model to retain this improvement when the context is not present. The method works by matching the model's output distribution under two settings: a student that sees only the question, and a self-teacher that also sees the context. What the model learns therefore depends on what context the self-teacher receives, yet the design of this context remains largely unexplored. We study context des
The rapid advancement of large language models necessitates continuous refinement techniques, and self-distillation is emerging as a critical method for efficient model improvement and deployment.
Improving self-distillation for language models enhances their efficiency and performance without additional context at inference, crucial for broader AI application and reducing inference costs.
The understanding and optimization of 'feedback alignment' in self-distillation will lead to more robust and higher-performing AI models that retain learned improvements more effectively.
- · AI developers
- · Cloud providers
- · Enterprise AI adopters
- · Companies with inefficient AI models
- · Competitors with less refined distillation techniques
More capable and efficient AI models become widely accessible.
Reduced operational costs for AI services, enabling a broader range of applications and accelerating AI integration into various sectors.
Enhanced AI capabilities could accelerate the development of more autonomous and sophisticated AI agents, further transforming industries.
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Read at arXiv cs.LG