The Era of Real-World Human Interaction: RL from User Conversations

Chuanyang Jin, Jing Xu, Bo Liu, Tao, Olga Golovneva, Tianmin Shu, Wenting Zhao, Xian Li, Jason Weston

The Era of Real-World Human Interaction: RL from User Conversations: 16 upvotes on Hugging Face Daily Papers, #25 of 82 papers on 2025-09-30. Day-by-day upvote history.

We posit that to achieve continual model improvement and multifaceted alignment, future models must learn from natural human interaction. Current conversational models are aligned using pre-annotated, expert-generated human feedback. In this work, we introduce Reinforcement Learning from Human Interaction (RLHI), a paradigm that learns directly from in-the-wild user conversations. We develop two complementary methods: (1) RLHI with User-Guided Rewrites, which revises unsatisfactory model outputs based on users' natural-language follow-up responses, (2) RLHI with User-Based Rewards, which learns via a reward model conditioned on knowledge of the user's long-term interaction history (termed persona). Together, these methods link long-term user personas to turn-level preferences via persona-conditioned preference optimization. Trained on conversations derived from WildChat, both RLHI variants outperform strong baselines in personalization and instruction-following, and similar feedback enhances performance on reasoning benchmarks. These results suggest organic human interaction offers scalable, effective supervision for personalized alignment.

Paper page on Hugging Face · arXiv

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