Interpretable Evaluation of Language Model Training Trajectories

Everyone has hypotheses of what is being learnt at various stages of LLM training. People design tests to prove or disprove said hypothesis. But is there a way to automatically discover fine-grained “concepts” being learnt at various stages of training without fine-grained hypotheses? What does this say about the training data? Can we do something about tokens seen at a step to control behavior?