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Amrith Setlur

Amrith Setlur has 2 papers in our library, in 2020–2024, most of them on Language models & pretraining, Reasoning; often with Adam Fisch, Alan W Black, Alekh Agarwal.

2 papers in our library · 2020–2024

OpenAlex · h-index 6 · 39 works · 243 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 256 times in our library · 7 more in our library without a page yet · ⭐ Paper Stars ranking: #5326

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

🎓Carnegie Mellon UniversityPittsburgh · United States2019–2026 · 23 paperscurrent🏢OpenAISan Francisco · United States2026 · 1 papercurrent🎓Stanford UniversityStanford · United States2023 · 2 papers🎓University of California, BerkeleyBerkeley · United States2023 · 2 papers

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 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 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20241 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Politeness Transfer: A Tag and Generate Approach (2020 · 235 citations)
    The authors frame politeness transfer as a new text style transfer task: rewriting non-polite action-directives, such as requests and commands, into polite versions without changing their meaning.
  2. Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning (2024 · 93 citations)
    Process reward models (PRMs) score every step of a reasoning trace instead of only the final answer. Step labels written by humans are expensive, though, and earlier automatically labelled PRMs gave only marginal gains over outcome reward models (ORMs). This work asks what a step-level reward should actually measure.…

All works

9 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. Politeness Transfer: A Tag and Generate ApproachAnnual Meeting of the Association for Computational Linguistics (ACL)Aman Madaan, Amrith Setlur, Tanmay Parekh, et al.2020 · 235 citations
  2. Rewarding Progress: Scaling Automated Process Verifiers for LLM ReasoningarXiv:2410.08146Amrith Setlur, Chirag Nagpal, Adam Fisch, et al.2024 · 93 citations
  3. Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning ↗arXiv (Cornell University) · no summary yetQu, Yuxiao et al.2025 · 30 citations
  4. Scaling Test-Time Compute Without Verification or RL is Suboptimal ↗arXiv:2502.12118 · no summary yetAmrith Setlur et al.2025 · 26 citations
  5. POPE: Learning to Reason on Hard Problems via Privileged On-Policy Exploration ↗arXiv (Cornell University) · no summary yetYuxiao Qu et al.2026 · 6 citations
  6. What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? ↗arXiv (Cornell University) · no summary yetKatie Kang et al.2024 · 4 citations
  7. RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems ↗arXiv (Cornell University) · no summary yetQu, Yuxiao et al.2025 · 3 citations
  8. Multitask Learning Can Improve Worst-Group Outcomes ↗arXiv (Cornell University) · no summary yetAtharva Kulkarni et al.2023 · 1 citation
  9. IsoCompute Playbook: Optimally Scaling Sampling Compute for LLM RL ↗arXiv (Cornell University) · no summary yetZhoujun Cheng 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 256 times (of the 149,439 papers whose bibliographies we read); 56 citing papers with a page here →.

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