dailyai.report

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