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Donghyun Kwak

Donghyun Kwak has 2 papers in our library, in 2024–2026, most of them on Reasoning; often with Jaewook Kang, Jeongyeon Nam, Jiwoo Hong.

2 papers in our library · 2024–2026

OpenAlex · h-index 10 · 26 works · 522 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 108 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7409

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

🏢NaverSeongnam-si · South Korea2017–2023 · 11 papers🏢NAVER CloudSeongnam-si · South Korea2021–2023 · 9 papers🎓Seoul National UniversitySeoul · South Korea2017–2025 · 4 papers🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2022 · 2 papers

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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. HyperCLOVA X Technical Report (2024 · 44 citations)
    Most large language models are built mainly for English and carry the cultural assumptions of English-speaking, especially North American, data.
  2. Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning (2026 · 22 citations)
    Reinforcement learning with verifiable rewards (RLVR) can improve LLM reasoning, but binary rewards are sparse.

All works

4 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. What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers ↗Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yetBoseop Kim et al.2021 · 119 citations
  2. HyperCLOVA X Technical ReportarXiv:2404.01954Kang Min Yoo, Jaegeun Han, Sookyo In, et al.2024 · 44 citations
  3. Online Difficulty Filtering for Reasoning Oriented Reinforcement LearningConference of the European Chapter of the Association for Computational Linguistics (EACL)Sanghwan Bae, Jiwoo Hong, Min Young Lee, et al.2026 · 22 citations
  4. Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild? ↗arXiv (Cornell University) · no summary yetSang-Woo Lee et al.2022 · 2 citations

 Co-authors here

5 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 108 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated

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