Incremental Preference Learning From Conversational Feedback

via “preference pair generation for rlhf training via sibling response comparison”

161K human-written messages in 35 languages with quality ratings.

Unique: Derives preferences from natural conversation branching and human ratings rather than synthetic comparison or LLM-based ranking. Grounds preference learning in actual human judgments without additional annotation.

vs others: More authentic preference signal than synthetic pairs (e.g., GPT-4 ranking) or single-response datasets. Enables preference learning at scale without expensive pairwise human annotation.