Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals (2505.18071v1)

Abstract: LLMs have demonstrated significant success in complex reasoning tasks such as math and coding. In contrast to these tasks where deductive reasoning predominates, inductive reasoning\textemdash the ability to derive 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 \textsc{AlignXplore}, a model that leverages extended reasoning chains to enable systematic preference inference from behavioral signals in users' interaction histories. We develop \textsc{AlignXplore} by combining cold-start training based on synthetic data with subsequent online reinforcement learning. Through extensive experiments, we demonstrate that \textsc{AlignXplore} achieves substantial improvements over the backbone model by an average of 11.05\% on in-domain and out-of-domain benchmarks, while maintaining 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.

Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals

The paper "Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals" presents a sophisticated approach to enhancing LLMs through personalized preference inference by leveraging extended inductive reasoning capabilities. This work addresses a pivotal yet challenging area in LLM alignment—capturing diverse and implicit user preferences using inductive reasoning, which has remained underexplored compared to deductive reasoning for tasks like mathematics and code generation.

Methodology and Model Design

The authors introduce AlignXplore, a novel framework designed to systematically infer user preferences from behavioral signals. The framework operates through extended reasoning chains, which are crucial for synthesizing implicit user signals into explicit preference descriptions. This task demands a deep inductive reasoning ability, as user preferences are often interspersed across multiple interaction forms and rarely articulated directly.

AlignXplore employs a two-stage training strategy:

1. Cold-Start Training: This phase uses synthetic data generated by advanced models to build initial reasoning capabilities. By generating high-quality examples demonstrating extended reasoning processes, the model can effectively learn to identify and extrapolate preference dimensions from initial data.
2. Reinforcement Learning: Using Group Relative Policy Optimization (GRPO), the model further refines its capability to generate preference descriptions that align with varying user-specific needs. The reinforcement learning phase capitalizes on rewards based on preference accuracy and reasoning coherence, reinforcing the model's ability to produce actionable and user-aligned outcomes.

Empirical Evaluation

The efficacy of AlignXplore is validated through extensive experiments across diverse benchmarks, including both in-domain and out-of-domain settings. The results indicate a substantial performance increase over baseline models, with an average improvement of 11.05% in preference inference accuracy. This demonstrates the model's strength in both terms of accuracy and generalization, achieving competitive results even against significantly larger models such as GPT-4.

Further analysis explores critical aspects of reward modeling strategies during training. It is shown that optimizing for preference judging, rather than response generation, offers more stable training signals and a progressive enhancement of inductive reasoning capabilities. This insight is crucial for designing effective alignment strategies that do not solely depend on explicit feedback or superficial correlations.

Implications and Future Directions

The implications of this research extend both theoretically and practically. AlignXplore sets a precedent for enhancing LLMs with robust inductive reasoning capabilities, opening pathways for more refined personalization techniques in AI systems. The ability to dynamically align with individual preferences can improve user satisfaction and reduce biases—an essential consideration in serving diverse user populations.

This investigation suggests potential avenues for future development in AI, where inductive reasoning can be further integrated into tasks requiring nuanced understanding of context and user behavior. The framework proposed could be adapted for other domains beyond preference inference, such as scientific research and unstructured data exploration.

In conclusion, this paper contributes a significant advancement in understanding and applying inductive reasoning within LLMs, laying the groundwork for future exploration and improvement in personalized AI alignment strategies. AlignXplore showcases the potential of integrative reasoning approaches in bridging implicit and explicit user models, ultimately enhancing AI's adaptability and performance across varied contexts.