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Zhouhao Sun

Zhouhao Sun has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Bibo Cai, Bing Qin, Li Du.

2 papers in our library · 2026

OpenAlex · h-index 3 · 15 works · 22 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1 time in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #11779

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

🎓Harbin Institute of TechnologyHarbin · China2023–2026 · 11 paperscurrent

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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 work

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization (2026 · 1 citation)
    GRPO aligns LLMs well when answers can be checked automatically, as in math or code. Open-ended generation has no such check, and its goals often conflict, for example creativity versus factuality. Existing fixes either rely on costly LLM judges or combine cheap heuristic rewards (perplexity, entropy, format) using…
  2. Large Language Models Are Still Misled by Simple Bias Ensembles (2026)
    Recent LLMs handle individual shortcut cues better than before. This paper argues that the real weakness appears when several such cues occur together in one input, which is common in real-world text. Most existing bias benchmarks inject only one bias per sample, and a few inject two. The authors therefore built…

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. MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Yang Zhao, Hepeng Wang, Xiao Ding, et al.2026 · 1 citation
  2. Large Language Models Are Still Misled by Simple Bias EnsemblesFindings of the Association for Computational LinguisticsZhouhao Sun, Zhiyuan Kan, Xiao Ding, et al.2026
  3. Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYang Zhao et al.2026

 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 1 time (of the 149,439 papers whose bibliographies we read).

Updated

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