Related search query recommendation is essential for enhancing user engagement and information discovery on digital platforms. While Large Language Models (LLMs) have shifted the field toward generative retrieval, existing methods suffer from two primary limitations: (1) pointwise generation via beam search often leads to semantic redundancy and wasted retrieval quota, and (2) current listwise approaches lack explicit reasoning, relying on superficial click-through rate (CTR) rewards. In this paper, we propose ReList, a novel framework that transforms related search into a reasoning-enhanced listwise generation task. ReList follows a two-stage training paradigm: first, Reasoning Activation constructs a high-quality dataset by back-translating diverse query lists into Chain-of-Thought (CoT) rationales; second, Alternative Training iteratively evolves the model using Reinforcement Learning with a Gated Multi-Objective Reward and a Corrective SFT mechanism to handle hard samples. Experimental results on real-world search benchmarks and online A/B tests demonstrate that ReList significantly outperforms state-of-the-art methods in both query diversity and user engagement, providing more insightful and logically grounded query recommendations.

Modern digital platforms rely on related search query recommendations to enhance engagement, yet existing methods fail to reconcile click-through rate (CTR) optimization with topic expansion. We propose **CMAQ**, a **C**onsistent **M**ulti-Objective **A**ligned **Q**uery generation framework that harmonizes these goals through three components: (1) reward modeling to quantify objectives, (2) style alignment for format compliance, and (3) consistency-aware optimization to coordinate joint improvements. CMAQ employs adaptive 𝛽-scaled DPO with geometric mean rewards, balancing CTR and expansion while mitigating objective conflicts. Extensive offline and online evaluations in a large-scale industrial setting demonstrate CMAQ’s superiority, achieving significant CTR gains (+2.3%) and higher human-rated query quality compared to state-of-the-art methods. Our approach enables high-quality query generation while sustaining user engagement and platform ecosystem health.