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Zhefan Wang

Zhefan Wang has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Reasoning; often with Chuhan Wu, Gang Li, Jinzhe Li.

2 papers in our library · 2024–2026

OpenAlex · ORCID · Wikidata · h-index 4 · 18 works · 123 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 15 times in our library · ⭐ Paper Stars ranking: #10323

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 · China2022–2026 · 8 paperscurrent🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2025–2026 · 5 paperscurrent🎓Xi'an Shiyou UniversityXi'an · China2024 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. StepTool: Enhancing Multi-Step Tool Usage in LLMs via Step-Grained Reinforcement Learning (2024 · 20 citations)
    Large language models still have trouble calling external APIs reliably over several turns. Most earlier work trains them with supervised fine-tuning on expert trajectories. That approach treats tool use as plain text generation and ignores that it is a sequence of decisions. StepTool instead casts tool learning as a…
  2. Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026)
    RLVR has improved LLM reasoning mainly in math and code, where answers can be checked automatically. Fields such as agriculture, law and medicine lack such checkable data, and answer-only rewards are sparse and tolerate flawed reasoning. The authors propose K2V (Knowledge-to-Verification) to address both gaps. K2V…

 Co-authors here

7 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 15 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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