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Tengyang Xie

Tengyang Xie has 2 papers in our library, in 2024, most of them on Language models & pretraining; often with Ahmed Hassan Awadallah, Arindam Mitra, Corby Rosset.

2 papers in our library · 2024

OpenAlex · h-index 9 · 36 works · 268 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 228 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6773

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 Wisconsin–MadisonMadison · United States2024–2026 · 9 paperscurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2018–2022 · 12 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts (2024 · 153 citations)
    Reward models (RMs) in RLHF usually learn from binary pairwise preferences with a Bradley-Terry objective.
  2. Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences (2024 · 44 citations)
    Standard RLHF, and contrastive shortcuts such as DPO, assume that preferences derive from a scalar reward (e.g., the Bradley-Terry model).

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. Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-ExpertsFindings of the Association for Computational LinguisticsHaoxiang Wang, Wei Xiong, Tengyang Xie, et al.2024 · 153 citations
  2. Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data ↗arXiv (Cornell University) · no summary yetFahim Tajwar et al.2024 · 54 citations
  3. Direct Nash Optimization: Teaching Language Models to Self-Improve with General PreferencesarXiv:2404.03715Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.2024 · 44 citations

 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 228 times (of the 149,439 papers whose bibliographies we read); 76 citing papers with a page here →.

Updated

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