dailyai.report

Targeted data filtering often fails to eliminate specific behaviors like liberal-lean or bold formatting during supervised fine-tuning. Researchers using OLMo found that removing problematic data points had little effect on the final model. This suggests that SFT traits emerge from broader patterns rather than individual samples, complicating efforts to scrub model biases.