ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914280512684032 |
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| author | Wang, Pei Wu, Yanan Song, Xiaoshuai Wang, Weixun Chen, Gengru Li, Zhongwen Yan, Kezhong Deng, Ken Liu, Qi Zhao, Shuaibing Xiong, Shaopan Liu, Xuepeng Chen, Xuefeng Deng, Wanxi Su, Wenbo Zheng, Bo |
| author_facet | Wang, Pei Wu, Yanan Song, Xiaoshuai Wang, Weixun Chen, Gengru Li, Zhongwen Yan, Kezhong Deng, Ken Liu, Qi Zhao, Shuaibing Xiong, Shaopan Liu, Xuepeng Chen, Xuefeng Deng, Wanxi Su, Wenbo Zheng, Bo |
| contents | Large language model (LLM)-based agents are increasingly deployed in e-commerce shopping. To perform thorough, user-tailored product searches, agents should interpret personal preferences, engage in multi-turn dialogues, and ultimately retrieve and discriminate among highly similar products. However, existing research has yet to provide a unified simulation environment that consistently captures all of these aspects, and always focuses solely on evaluation benchmarks without training support. In this paper, we introduce ShopSimulator, a large-scale and challenging Chinese shopping environment. Leveraging ShopSimulator, we evaluate LLMs across diverse scenarios, finding that even the best-performing models achieve less than 40% full-success rate. Error analysis reveals that agents struggle with deep search and product selection in long trajectories, fail to balance the use of personalization cues, and to effectively engage with users. Further training exploration provides practical guidance for overcoming these weaknesses, with the combination of supervised fine-tuning (SFT) and reinforcement learning (RL) yielding significant performance improvements. Code and data will be released at https://github.com/ShopAgent-Team/ShopSimulator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18225 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants Wang, Pei Wu, Yanan Song, Xiaoshuai Wang, Weixun Chen, Gengru Li, Zhongwen Yan, Kezhong Deng, Ken Liu, Qi Zhao, Shuaibing Xiong, Shaopan Liu, Xuepeng Chen, Xuefeng Deng, Wanxi Su, Wenbo Zheng, Bo Artificial Intelligence Large language model (LLM)-based agents are increasingly deployed in e-commerce shopping. To perform thorough, user-tailored product searches, agents should interpret personal preferences, engage in multi-turn dialogues, and ultimately retrieve and discriminate among highly similar products. However, existing research has yet to provide a unified simulation environment that consistently captures all of these aspects, and always focuses solely on evaluation benchmarks without training support. In this paper, we introduce ShopSimulator, a large-scale and challenging Chinese shopping environment. Leveraging ShopSimulator, we evaluate LLMs across diverse scenarios, finding that even the best-performing models achieve less than 40% full-success rate. Error analysis reveals that agents struggle with deep search and product selection in long trajectories, fail to balance the use of personalization cues, and to effectively engage with users. Further training exploration provides practical guidance for overcoming these weaknesses, with the combination of supervised fine-tuning (SFT) and reinforcement learning (RL) yielding significant performance improvements. Code and data will be released at https://github.com/ShopAgent-Team/ShopSimulator. |
| title | ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.18225 |