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Melody Y. Guan

Melody Y. Guan has 3 papers in our library, in 2018–2025, most of them on Reasoning, Language models & pretraining, Bias, fairness & safety; often with Alec Helyar, Aleksander Madry, Andrea Vallone.

3 papers in our library · 2018–2025

OpenAlex · h-index 12 · 19 works · 2,391 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1,718 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #2951

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

🎓Stanford UniversityStanford · United States2017–2025 · 13 paperscurrent🏢OpenAISan Francisco · United States2025 · 1 papercurrent

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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 1 workSafety Research · 2 worksSocial Sciences · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 2 worksBias, fairness & safety · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20251 paper20241 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Efficient Neural Architecture Search via Parameter Sharing (2018 · 699 citations)
    Neural Architecture Search (NAS) can find strong models, but it is very costly: each candidate network is trained to convergence, scored, and then its weights are thrown away.
  2. Deliberative Alignment: Reasoning Enables Safer Language Models (2024 · 115 citations)
    Conventional safety training (SFT, RLHF) has two weaknesses. LLMs answer instantly without deliberating, and they infer safety rules only indirectly from labeled examples. As a result, they can be jailbroken yet still refuse harmless requests. OpenAI researchers propose Deliberative Alignment, which teaches the model…
  3. Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation (2025 · 63 citations)
    The paper asks whether the reasoning traces of RL-trained language models can expose reward hacking, where an agent exploits flaws in its objective instead of solving the task.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Efficient Neural Architecture Search via Parameters Sharing ↗International Conference on Machine Learning · no summary yetHieu Pham et al.2018 · 1,209 citations
  2. Efficient Neural Architecture Search via Parameter SharingarXiv:1802.03268Hieu Pham, Melody Y. Guan, Barret Zoph, et al.2018 · 699 citations
  3. Deliberative Alignment: Reasoning Enables Safer Language ModelsarXiv:2412.16339Melody Y. Guan, Manas Joglekar, Eric Wallace, et al.2024 · 115 citations
  4. Monitoring Reasoning Models for Misbehavior and the Risks of Promoting ObfuscationarXiv:2503.11926Bowen Baker, Joost Huizinga, Leo Gao, et al.2025 · 63 citations
  5. Faster Discovery of Neural Architectures by Searching for Paths in a Large Model ↗International Conference on Learning Representations · no summary yetHieu Pham et al.2018 · 15 citations

 Co-authors here

12 of 18 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 1,718 times (of the 149,439 papers whose bibliographies we read); 433 citing papers with a page here →.

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