Training on a model’s own incorrect samples could beat distilling from a much larger teacher

A user claims training a model on its own incorrect samples can beat distilling from a much larger teacher. They argue that strategically sampling for approach-level diversity yields better pass@k, test-time scaling and RL initialization than traditional RFT.