Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal
Researchers have developed a new data-centric pipeline for post-training language models that uses interpretability to understand and shape the learning signal. This method allows for the inspection of preference datasets before optimization, enabling fine-grained user feedback on desired behaviors. The pipeline can diagnose undesirable signals in existing data, mitigate off-target learning, and amplify specific model properties like safeguards and personality. AI
IMPACT Enables more controlled and transparent shaping of AI behavior by auditing the learning signal itself.