Climber-Pilot: A Non-Myopic Generative Recommendation Model Towards Better Instruction-Following

• URL: http://arxiv.org/abs/2602.13581v1
• Date: Sat, 14 Feb 2026 03:46:06 GMT
• Title: Climber-Pilot: A Non-Myopic Generative Recommendation Model Towards Better Instruction-Following
• Authors: Da Guo, Shijia Wang, Qiang Xiao, Yintao Ren, Weisheng Li, Songpei Xu, Ming Yue, Bin Huang, Guanlin Wu, Chuanjiang Luo,
• Abstract summary: We present Climber-Pilot, a unified generative retrieval framework.<n>We introduce Time-Aware Multi-Item Prediction (TAMIP), a novel training paradigm designed to mitigate inherent myopia in generative retrieval.<n>We also propose Condition-Guided Sparse Attention (CGSA), which incorporates business constraints directly into the generative process via sparse attention.
• Score: 19.550149895505683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative retrieval has emerged as a promising paradigm in recommender systems, offering superior sequence modeling capabilities over traditional dual-tower architectures. However, in large-scale industrial scenarios, such models often suffer from inherent myopia: due to single-step inference and strict latency constraints, they tend to collapse diverse user intents into locally optimal predictions, failing to capture long-horizon and multi-item consumption patterns. Moreover, real-world retrieval systems must follow explicit retrieval instructions, such as category-level control and policy constraints. Incorporating such instruction-following behavior into generative retrieval remains challenging, as existing conditioning or post-hoc filtering approaches often compromise relevance or efficiency. In this work, we present Climber-Pilot, a unified generative retrieval framework to address both limitations. First, we introduce Time-Aware Multi-Item Prediction (TAMIP), a novel training paradigm designed to mitigate inherent myopia in generative retrieval. By distilling long-horizon, multi-item foresight into model parameters through time-aware masking, TAMIP alleviates locally optimal predictions while preserving efficient single-step inference. Second, to support flexible instruction-following retrieval, we propose Condition-Guided Sparse Attention (CGSA), which incorporates business constraints directly into the generative process via sparse attention, without introducing additional inference steps. Extensive offline experiments and online A/B testing at NetEase Cloud Music, one of the largest music streaming platforms, demonstrate that Climber-Pilot significantly outperforms state-of-the-art baselines, achieving a 4.24\% lift of the core business metric.

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