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OneRec-Think: In-Text Reasoning for Generative Recommendation

Paper · 2026 · Zhanyu Liu, Shiyao Wang, Xingmei Wang et al.

Summary

Generative recommenders such as OneRec decode item identifiers directly. This makes them implicit predictors: they cannot produce explicit, controllable reasoning the way modern LLMs do. OneRec-Think, developed at Kuaishou, addresses this by placing dialogue, textual reasoning and personalized recommendation in a single autoregressive model. Given a prompted user history, the model first writes a reasoning trace and then emits the itemic tokens of the next item.

Training proceeds in three stages:

  • Itemic Alignment: multi-task pre-training grounds discrete item tokens in the LLM's text space.
  • Reasoning Activation: Reasoning Scaffolding elicits reasoning in the recommendation setting.
  • Reasoning Enhancement: reinforcement learning uses a reward tailored to the fact that many items can be valid choices for a user.

For production serving, the authors propose a "Think-Ahead" inference architecture. The method reportedly reaches state-of-the-art results on public benchmarks. An online deployment on Kuaishou increased APP Stay Time. This shows that interpretable, conversational recommendation with explicit reasoning can work at industrial scale.

Key points

  • Items are represented as sequences of itemic tokens, following OneRec and TIGER (RQ-VAE-style hierarchical codes), built from multimodal and collaborative signals.
  • Itemic Alignment uses four complementary next-token-prediction tasks, including Interleaved User Persona Grounding. This task mixes item tokens with textual persona data: static attributes, searches, interaction sequences and summarized interests.
  • In the Kuaishou deployment, the deployable "Think-Ahead" architecture yielded a 0.159% gain in APP Stay Time.
  • The dialogic design lets users steer recommendations with explicit requests, for example asking for cheerful content.
  • Limitation: public datasets have short behavior sequences and small item spaces, so the authors used a simplified reasoning setup for those benchmarks. They are building a larger benchmark to address this.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢KuaishouBeijing · China15 works · 95 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,426 works

Cite

Cite as Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai and Guorui Zhou. 2026. OneRec-Think: In-Text Reasoning for Generative Recommendation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.123

@inproceedings{liu2026onerec,
  title = {{OneRec-Think: In-Text Reasoning for Generative Recommendation}},
  author = {Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai and Guorui Zhou},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.123},
  url = {https://doi.org/10.18653/v1/2026.acl-long.123}
}

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