Preference Fine-Tuning With Suboptimal Data Improves LLM Alignment
Preference fine-tuning of LLMs should leverage suboptimal, on-policy data instead of relying solely on expert-curated data for better alignment with human preferences.
This is a Plain English Papers summary of a research paper called Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter. Overview The paper explores preference fine-tuning of large language models (LLMs), which aims to align the models' outputs with human preferences. The authors argue that current preference fine-tuning methods should leverage suboptimal, on-policy data (i.e., data generated by the model during deployment) rather than relying solely o...