🧑‍🔬 Guorui Zhou

🏛️ Affiliations

🏢KuaishouBeijing · China2023–2026 · 40 papersEmploymentcurrent🔬China Academy of Engineering PhysicsMianyang · China2014–2025 · 49 papers🔬Laser Fusion Research CenterMianyang2014–2025 · 45 papers🏢Alibaba GroupHangzhou · China2017–2022 · 19 papers🎓Sichuan UniversityChengdu · China2011–2023 · 5 papers🤝Chinese Research Academy of Environmental SciencesBeijing · China2024–2025 · 3 papers🎓Beijing University of Posts and TelecommunicationsBeijing · China2015–2016 · 3 papers🎓Northumbria UniversityNewcastle upon Tyne · United Kingdom2017–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoning (2026 · 15 citations)
   Standard RLVR algorithms such as GRPO give every token in a sampled response the same advantage. They cannot tell pivotal reasoning decisions from routine ones, so the policy settles too early on a few safe trajectories and suffers entropy collapse. The authors, from Kuaishou Technology, propose UCAS…
2. CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning (2026 · 7 citations)
   The paper tackles unstable policy entropy when large language models are trained with RL for reasoning. PPO-style methods, including GRPO and DAPO, clip the importance sampling ratio, and this silently drops gradients from low-probability tokens. The authors split tokens by advantage sign and probability. They argue…
3. Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement Learning (2026 · 1 citation)
   LLM post-training with reinforcement learning is usually off-policy, so updates use samples from older policies.
4. DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey Writing (2026)
   Deep Research agents are usually benchmarked on how well they find information. The stage after retrieval gets far less objective testing: merging evidence from many sources into a long, organized report. DeepSynth-Eval (DSE) targets that stage. The authors treat expert-written survey papers as reference answers. For…
5. Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (2026)
   LLM-based generative recommenders must name items in a form the model can produce and that maps back to real catalog entries.

All works

7 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

1. Deep Interest Evolution Network for Click-Through Rate Prediction ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetGuorui Zhou et al.2019 · 1,102 citations
2. Leanabell-Prover: Posttraining Scaling in Formal Reasoning ↗arXiv:2504.06122 · no summary yetJingyuan Zhang et al.2025 · 21 citations
3. Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper ReasoningFindings of the Association for Computational LinguisticsCan Xie, Ruotong Pan, Xiangyu Wu, et al.2026 · 15 citations
4. CE-GPPO: Coordinating Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Zhenpeng Su, Leiyu Pan, Minxuan Lv, et al.2026 · 7 citations
5. Entropy Ratio Clipping as a Soft Global Constraint for Stable Reinforcement LearningFindings of the Association for Computational LinguisticsZhenpeng Su, Leiyu Pan, Minxuan Lv, et al.2026 · 1 citation
6. DeepSynth-Eval: Objectively Evaluating Information Consolidation in Deep Survey WritingFindings of the Association for Computational LinguisticsHongzhi Zhang, Yuanze Hu, Tinghai Zhang, et al.2026
7. Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term IdentifiersFindings of the Association for Computational LinguisticsZhiyang Zhang, Junda She, Kuo Cai, et al.2026

🤝 Co-authors here

12 of 17 co-authors with a page, the most papers together first.

• Kun Gai ×4
• Ruiming Tang ×3
• Han Li ×2
• Leiyu Pan ×2
• Minxuan Lv ×2
• Wenping Hu ×2
• Yuntao Li ×2
• Zhenpeng Su ×2
• Bo Chen
• Fuzheng Zhang
• Shiyao Wang
• Tao Wang

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×4: Findings of the Association for Computational Linguistics 2026 ×4
• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026
• Proceedings of the AAAI Conference on Artificial Intelligence

🔗 Cited by

Papers of our library cite their works 88 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated 2 November 2026

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