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Jinyang Wu

Jinyang Wu has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning; often with Jianhua Tao, Shuai Zhang, Zhengqi Wen.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 13 · 63 works · 435 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 38 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7909

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Tsinghua UniversityBeijing · China2024–2026 · 14 paperscurrent🎓Shanghai Jiao Tong UniversityShanghai · China2013–2025 · 32 papers🏥Shanghai Ninth People's HospitalShanghai · China2013–2025 · 31 papers🎓Xiangtan UniversityXiangtan · China2023–2024 · 4 papers🎓Zaozhuang UniversityZaozhuang · China2025 · 2 papers🏥Shanghai Stomatological HospitalShanghai · China2020–2022 · 2 papers🏥Tianjin Medical University Cancer Institute and HospitalTianjin · China2020–2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 works

 Works by year

2020s

20263 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models (2025 · 19 citations)
    RAG is widely used to reduce LLM hallucinations, but real retrieval results are messy. Earlier robustness studies looked at only a few noise types, usually three or fewer, and assumed noise always hurts. This paper builds a broader picture. It defines seven retrieval noise types grounded in linguistics and releases…
  2. SPARK: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic Learning (2026 · 11 citations)
    Training LLM agents with reinforcement learning on long, multi-step tasks is difficult because successful trajectories are rare, especially when compute is limited.
  3. ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex Reasoning (2026 · 8 citations)
    As both LLMs and external tools multiply, choosing which model should handle a query and with which tool becomes a large combinatorial search problem.
  4. TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning (2026 · 8 citations)
    RL methods such as GRPO strengthen LLM reasoning, but they learn only from rollouts the model samples freely from its own distribution.

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jinyang Wu, Shuai Zhang, Feihu Che, et al.2025 · 19 citations
  2. SPARK: Strategic Policy-Aware Exploration via Dynamic Branching for Long-Horizon Agentic LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Jinyang Wu, Shuo Yang, Yuhao Shen, et al.2026 · 11 citations
  3. ATLAS: Orchestrating Heterogeneous Models and Tools for Multi-Domain Complex ReasoningFindings of the Association for Computational LinguisticsJinyang Wu, Guocheng Zhai, Ruihan Jin, et al.2026 · 8 citations
  4. TemplateRL: Structured Template-Guided Reinforcement Learning for LLM ReasoningFindings of the Association for Computational LinguisticsJinyang Wu, Chonghua Liao, Mingkuan Feng, et al.2026 · 8 citations
  5. Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetJinyang Wu et al.2026

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 38 times (of the 149,439 papers whose bibliographies we read); 15 citing papers with a page here →.

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