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Ruida Wang

Ruida Wang has 2 papers in our library, in 2025, most of them on Reasoning, Language models & pretraining; often with Tong Zhang, Yuxin Li, Jipeng Zhang.

2 papers in our library · 2025

OpenAlex · ORCID · h-index 2 · 7 works · 22 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 62 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8761

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

🎓University of Illinois Urbana-ChampaignUrbana · United States2025–2026 · 11 papersEmployment since 2026 · Ph.D Student, Siebel School of Computing and Data Sciencecurrent🎓Hong Kong University of Science and TechnologyHong Kong · China2023–2026 · 8 paperscurrent🎓University of Wisconsin–MadisonMadison · United States2024–2025Employment 2024–2025 · Ph.D. student, Computer Science

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

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputational Theory and Mathematics · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving (2025 · 18 citations)
    The paper addresses automated theorem proving in Lean4. Current LLM approaches fall into two camps. Step-by-step tree search uses verifier feedback but is costly and lacks high-level planning. Whole-proof generation exploits natural-language reasoning but never sees intermediate verifier signals. MA-LoT bridges these…
  2. Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability (2025 · 7 citations)
    Reinforcement learning on base models has improved LLM math reasoning, but it mostly sharpens skills the model already has.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts ↗arXiv:2407.03203 · no summary yetRuida Wang et al.2024 · 51 citations
  2. MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem ProvingarXiv:2503.03205Ruida Wang, Rui Pan, Yuxin Li, et al.2025 · 18 citations
  3. Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math CapabilityConference on Empirical Methods in Natural Language Processing (EMNLP)Ruida Wang, Yuxin Li, Yi R. Fung, et al.2025 · 7 citations

 Co-authors here

8 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 62 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated

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