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Huaiyuan Ying

Huaiyuan Ying has 3 papers in our library, in 2024, most of them on Reasoning, Language models & pretraining; often with Zijian Wu, Dahua Lin, Kai Chen.

3 papers in our library · 2024

OpenAlex · ORCID · h-index 6 · 18 works · 193 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 537 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6154

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 · China2021–2026 · 12 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksComputational Theory and Mathematics · 3 worksArts and Humanities · 1 workMathematics · 1 workLanguage and Linguistics · 1 workApplied Mathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 3 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20243 papers

 Papers

The most cited first (OpenAlex and our library).

  1. InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning (2024 · 90 citations)
    InternLM-Math is a family of open math-specialised LLMs from Shanghai AI Laboratory and partner universities.
  2. Lean Workbook: A large-scale Lean problem set formalized from natural language math problems (2024 · 80 citations)
    Large language models do well on informal math but poorly on formal theorem proving in Lean. A main reason is the lack of formal training data: MiniF2F is small, and Mathlib mostly covers foundational theorems rather than competition problems. The authors built an active-learning autoformalization pipeline that…
  3. InternLM2.5-StepProver: Advancing Automated Theorem Proving via Critic-Guided Search (2024 · 59 citations)
    Most LLM theorem provers for LEAN build a proof one tactic at a time. They pick which proof state to expand next using the prover's own log-probabilities. That signal gets unreliable as proofs get longer, so these systems rarely find deep proofs. The authors add a separate critic model that scores each proof state.…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. InternLM-Math: Open Math Large Language Models Toward Verifiable ReasoningarXiv:2402.06332Huaiyuan Ying, Shuo Zhang, Linyang Li, et al.2024 · 90 citations
  2. Lean Workbook: A large-scale Lean problem set formalized from natural language math problemsConference on Neural Information Processing Systems (NeurIPS)Huaiyuan Ying, Zijian Wu, Yihan Geng, et al.2024 · 80 citations
  3. InternLM2.5-StepProver: Advancing Automated Theorem Proving via Critic-Guided SearcharXiv:2410.15700Zijian Wu, Suozhi Huang, Zhejian Zhou, et al.2024 · 59 citations
  4. Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving ↗arXiv:2507.23726 · no summary yetLuoxin Chen et al.2025 · 46 citations

 Co-authors here

12 of 15 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 537 times (of the 149,439 papers whose bibliographies we read); 124 citing papers with a page here →.

Updated

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