dailyai.report

Filtering training data for specific behaviors, such as liberal lean or bold formatting, rarely removes those traits from OLMo models. Researchers found that targeted data removal during supervised fine-tuning has little effect on final model behavior. This suggests that simple dataset curation cannot reliably scrub undesirable traits. Practitioners must find more robust alignment methods.