dailyai.report

Targeted data filtering often fails to eliminate undesirable behaviors during supervised fine-tuning. Researchers using OLMo found that removing specific data points didn't stop models from adopting traits like liberal-lean or specific formatting habits. This suggests that SFT behaviors emerge from broader patterns rather than isolated examples. Practitioners cannot rely on simple filtering for alignment.