Skill Distillation (tomtunguz.com)

In a recent development in AI, a new approach called "skill distillation" has emerged, allowing smaller models to learn from advanced frontier models like Opus 4.7 and GPT-5.1. This system is implemented in a personal agent designed to manage multiple tasks such as email, scheduling, and research through an iterative learning process. At its core, the agent utilizes a three-layer architecture: a local knowledge base (QMD) of procedural workflows, atomic skill files authored and evaluated by the frontier models, and an Agent Loop that integrates with external APIs to refine its operations continuously. What makes skill distillation particularly significant is its potential to bridge the knowledge gap between large and small models without the traditional reliance on compressed outputs or prompt-response tuning. Instead, it focuses on teaching procedural tasks, allowing the smaller model to execute steps effectively without needing to understand the underlying complexities. This approach not only enables more efficient use of resources by letting cheaper models take on operational tasks, but it also builds a structure for institutional knowledge that can be dynamically updated as new skills emerge. Such advancements could significantly enhance productivity in AI-driven environments, positioning skill distillation as a pivotal technique in the future of AI training methodologies.