arXiv:2605.23493v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has gained wide attraction as an LLM post-training paradigm due to its effectiveness in improving capabilities without introducing model distribution drift, and consequently, regression in general tasks. On-Policy Self-Distillation (OPSD) is an efficient use-case of OPD, which is appealing as it requires only a single model as a student and teacher, and it also has the benefit of providing privileged context that is a absent at inference time (e.g. a persona, a private fact, or a worked solution) to the teacher during
The continuous evolution of LLM capabilities and the desire for more efficient and robust post-training paradigms drive the development of techniques like On-Policy Distillation.
This development allows LLMs to internalize and effectively use privileged context during training, leading to improved performance without compromising general task abilities.
The method of 'distilling' knowledge into LLMs now includes more sophisticated ways to integrate context available during training but absent at inference, making models more capable for specific applications.
- · AI model developers
- · Companies using LLMs for complex tasks
- · AI research institutions
- · Developers relying on less efficient LLM fine-tuning methods
Improved performance and efficiency of LLMs in specialized applications due to better contextual understanding.
Reduced computational costs for achieving high performance in specific tasks as models become more adept at internalizing context.
Acceleration of AI agent development, as these models can more effectively leverage private knowledge or personas.
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Read at arXiv cs.AI