Title: Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: reasoning, personalized, preference alignment

Abstract: While large language models (LLMs) excel at deductive reasoning tasks such as math and coding, their capacity for inductive reasoning, which involves deriving general rules from incomplete evidence, remains underexplored. This paper investigates extended inductive reasoning in LLMs through the lens of personalized preference inference, a critical challenge in LLM alignment where current approaches struggle to capture diverse user preferences. The task demands strong inductive reasoning capabilities as user preferences are typically embedded implicitly across various interaction forms, requiring models to synthesize consistent preference patterns from scattered signals. We propose AlignXplore, a model that leverages extended reasoning chains to enable systematic preference inference from behavioral signals in users' interaction histories. Such explicit preference articulation enables efficient streaming inference: when new behavioral signals emerge, the model can directly build upon previously inferred preference descriptions rather than reprocessing historical signals from scratch, while also supporting iterative refinement to the inferred preferences. We develop AlignXplore by combining cold-start training based on synthetic data with subsequent online reinforcement learning. Extensive experiments demonstrate that AlignXplore achieves substantial improvements over the backbone model by an average of 15.49\% on both in-domain and out-of-domain benchmarks, while maintaining a strong generalization ability across different input formats and downstream models. Further analyses establish best practices for preference inference learning through systematic comparison of reward modeling strategies, while revealing the emergence of human-like inductive reasoning patterns during training.