dailyai.report

Seven hypotheses explain why filtering undesirable rollouts during supervised fine-tuning fails, according to Google DeepMind. Researchers identified hereditary traits in Gemini, including negative emotion and date confusion, that persist despite filtering. This suggests that simple data removal cannot reliably scrub problematic model behaviors. Practitioners must seek more robust alignment methods beyond basic SFT filtering.