Existing benchmarks in e-commerce primarily focus on basic user intents, such as finding or purchasing products. However, real-world users often pursue more complex goals, such as applying vouchers, managing budgets, and finding multi-products seller. To bridge this gap, we propose ShoppingBench, a novel end-to-end shopping benchmark designed to encompass increasingly challenging levels of grounded intent. Specifically, we propose a scalable framework to simulate user instructions based on various intents derived from sampled real-world products. To facilitate consistent and reliable evaluations, we provide a large-scale shopping sandbox that serves as an interactive simulated environment, incorporating over 2.5 million real-world products. Experimental results demonstrate that even state-of-the-art language agents (such as GPT-4.1) achieve absolute success rates under 50% on our benchmark tasks, highlighting the significant challenges posed by our ShoppingBench. In addition, we propose a trajectory distillation strategy and leverage supervised fine-tuning, along with reinforcement learning on synthetic trajectories, to distill the capabilities of a large language agent into a smaller one. As a result, our trained agent achieves competitive performance compared to GPT-4.1.

翻译：现有电子商务基准主要关注基础用户意图，例如查找或购买商品。然而，真实世界用户往往追求更复杂的目标，例如应用优惠券、管理预算以及寻找多商品卖家。为弥补这一差距，我们提出了ShoppingBench，一个新颖的端到端购物基准，旨在涵盖日益复杂的具象化意图层级。具体而言，我们提出一个可扩展的框架，基于从真实世界商品样本中提取的多样化意图来模拟用户指令。为促进一致且可靠的评估，我们提供了一个大规模购物沙箱作为交互式模拟环境，整合了超过250万种真实世界商品。实验结果表明，即使是最先进的语言智能体（如GPT-4.1）在我们的基准任务上的绝对成功率也低于50%，凸显了ShoppingBench带来的显著挑战。此外，我们提出一种轨迹蒸馏策略，利用监督微调并结合合成轨迹的强化学习，将大型语言智能体的能力蒸馏至较小模型中。最终，我们训练的智能体相较于GPT-4.1实现了具有竞争力的性能表现。