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Zhang-Wei Hong

Zhang-Wei Hong has 2 papers in our library, in 2025–2026, most of them on Reasoning, Language models & pretraining; often with Chuang Gan, Guangtao Zeng, David Cox.

2 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 9 · 47 works · 772 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 24 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #11726

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

🎓Massachusetts Institute of TechnologyCambridge · United States2021–2026 · 26 paperscurrent🏢IBMArmonk · United States2025–2026 · 5 paperscurrent🎓University of California, Los AngelesLos Angeles · United States2026 · 1 papercurrent🎓National Tsing Hua UniversityHsinchu · Taiwan2017–2021 · 8 papers🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2022–2024 · 3 papers🏢Preferred NetworksTokyo · Japan2020–2021 · 2 papers🎓The University of SydneySydney · Australia2024Education since 2024

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 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20261 paper20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search (2025 · 13 citations)
    Most ways of boosting LLM reasoning at inference time rely on heavy sampling steered by a separate verifier or reward model.
  2. Tailored Primitive Initialization is the Secret Key to Reinforcement Learning (2026)
    RL fine-tuning of LLMs for reasoning is sample-inefficient, and its gains depend heavily on the base model.

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. Tactics of Adversarial Attack on Deep Reinforcement Learning Agents ↗International Joint Conference on Artificial Intelligence · no summary yetYen-Chen Lin et al.2017 · 300 citations
  2. A multimodal robotic platform for multi-element electrocatalyst discovery ↗Nature · no summary yetZhen Zhang et al.2025 · 73 citations
  3. Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive SearcharXiv:2502.02508Maohao Shen, Guangtao Zeng, Zhenting Qi, et al.2025 · 13 citations
  4. Tailored Primitive Initialization is the Secret Key to Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yihang Yao, Guangtao Zeng, Raina Wu, et al.2026

 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 24 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

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