Panoramic View of Strategy Distillation for Large Language Models: A Resource Compilation from Theory to Practice

This article introduces a curated resource library on strategy distillation for large language models, covering relevant papers, technical reports, frameworks, and tools, providing researchers and developers with a systematic learning path. As a key model compression technique, strategy distillation focuses on transferring the decision-making strategy of models rather than just imitating output probabilities, making it an important direction to address the deployment cost issues of large models.

With the growth of parameter scales in large language models, model compression has become a core challenge in AI engineering. Traditional distillation struggles to capture the complex decision-making logic of LLMs, leading to the emergence of strategy distillation technology—focusing on transferring the decision-making strategies of models (such as reasoning chains and context utilization) rather than just imitating output probabilities, which gives it unique advantages in preserving model capabilities.

The value of the 'Awesome On-Policy Distillation' resource library maintained by Chris Liu lies in: 1. Systematic classification (theory, algorithms, applications, tools) lowers the learning threshold; 2. Curated principles ensure content quality and save screening time; 3. Continuous updates and community maintenance keep it up-to-date.

The main technical routes of strategy distillation include: 1. Reinforcement learning-based distillation (modeled as an RL problem to handle non-differentiable decisions); 2. Contrastive learning-based distillation (distinguishing between teacher-preferred and non-preferred outputs); 3. Multi-stage progressive distillation (gradually building capabilities in a curriculum learning manner); 4. Domain-specific adaptation (for scenarios like code generation and mathematical reasoning).

The resource library includes practical open-source tools: training frameworks (supporting distributed distillation), evaluation tools (automated test suites), datasets (annotated data for distillation research), and pre-trained models (lightweight models), lowering the technical threshold.

Application scenarios of strategy distillation include: edge device deployment (localized intelligent interaction), real-time service optimization (low latency), domain-specific models (industries like healthcare/law), and multi-modal expansion (lightweighting of vision-language models).

Open issues in the field of strategy distillation: 1. Quantifying and minimizing the boundary of capability loss; 2. Simultaneous distillation of multiple tasks; 3. Dynamically adjusting distillation strategies; 4. Lack of systematic theoretical explanations to guide algorithm design.

The 'Awesome On-Policy Distillation' provides a knowledge map to help democratize AI capabilities. It is an ideal starting point for researchers and offers technical options for engineering teams. We look forward to strategy distillation playing a role in more scenarios, making AI serve society in a more lightweight and economical way.