Elamur Research⌕
☰
home / authors / Jiantao Jiao

Jiantao Jiao

Jiantao Jiao has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining; often with Anastasios N. Angelopoulos, Banghua Zhu, Evan Frick.

2 papers in our library · 2024–2025

OpenAlex · ORCID · h-index 20 · 127 works · 2,509 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 96 times in our library · 8 more in our library without a page yet · ⭐ Paper Stars ranking: #8528

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 California, BerkeleyBerkeley · United States2014–2026 · 67 paperscurrent🏢NvidiaSanta Clara · United States2025–2026 · 4 paperscurrent🏥UC San Diego Health SystemSan Diego · United States2026 · 1 papercurrent🎓University of California San DiegoSan Diego · United States2026 · 1 papercurrent🎓Stanford UniversityStanford · United States2012–2023 · 34 papers🎓Tsinghua UniversityBeijing · China2010–2014 · 4 papers🎓Hong Kong University of Science and TechnologyHong Kong · China2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge (2025 · 64 citations)
    Self-Rewarding training lets an LLM improve without human labels by scoring its own outputs. However, it trains only the model's answering skill. Its evaluation skill stays fixed, so gains plateau after a few iterations and the model risks reward hacking. The authors add a third role, a meta-judge, alongside the…
  2. How to Evaluate Reward Models for RLHF (2024 · 31 citations)
    Reward models guide RLHF, but the only direct way to judge one is to train a policy with it and measure the result.

All works

10 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. Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-JudgeConference on Empirical Methods in Natural Language Processing (EMNLP)Tianhao Wu, Weizhe Yuan, Olga Golovneva, et al.2025 · 64 citations
  2. Generalized resilience and robust statistics ↗The Annals of Statistics · no summary yetBanghua Zhu, Jiantao Jiao, Jacob Steinhardt2019 · 34 citations
  3. How to Evaluate Reward Models for RLHFarXiv:2410.14872Evan Frick, Tianle Li, Connor Chen, et al.2024 · 31 citations
  4. Robust estimation via generalized quasi-gradients ↗Information and Inference A Journal of the IMA · no summary yetBanghua Zhu, Jiantao Jiao, Jacob Steinhardt2021 · 22 citations
  5. Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures ↗arXiv (Cornell University) · no summary yetHongyang Zhang et al.2018 · 17 citations
  6. When does the Tukey Median work? ↗IEEE International Symposium on Information Theory (ISIT) · no summary yetBanghua Zhu, Jiantao Jiao, Jacob Steinhardt2020 · 16 citations
  7. On Optimal Caching and Model Multiplexing for Large Model Inference ↗arXiv (Cornell University) · no summary yetBanghua Zhu et al.2023 · 5 citations
  8. Towards Optimal Caching and Model Selection for Large Model Inference ↗neural information processing systems · no summary yetBanghua Zhu et al.2023 · 2 citations
  9. Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees ↗arXiv (Cornell University) · no summary yetBanghua Zhu et al.2022 · 1 citation
  10. Before They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models ↗arXiv (Cornell University) · no summary yetTan Yu et al.2026

 Co-authors here

12 of 14 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 96 times (of the 149,439 papers whose bibliographies we read); 47 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jiantao-jiao/