Distillation in 2026 (so far): which frontier models use it and how

In 2026, distillation is a key technique used in frontier AI models to optimize performance and efficiency by compressing large models into smaller ones, merging reinforcement learning (RL) experts into a unified model, and facilitating self-improvement within models. The process involves various stages, such as off-policy, on-policy, and self-distillation, each serving different purposes like matching a smaller student model to a large teacher model or integrating domain-specific RL experts into a single model. On-policy distillation, in particular, emphasizes training a student model by having it generate rollouts while receiving token-level feedback from multiple specialized teachers, often not larger but more specialized than the student. This approach is preferred over traditional RL due to its faster convergence and reduced computational cost. Additionally, self-distillation allows a model to learn from an improved version of itself by conditioning on hints that guide its behavior during training, enabling continual learning without forgetting previously acquired knowledge.

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