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Pranjal Aggarwal

Pranjal Aggarwal has 2 papers in our library, in 2024–2025, most of them on Reasoning, Code & programming languages; often with Sean Welleck.

2 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 5 · 30 works · 207 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 137 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #7697

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 States2025–2026 · 5 paperscurrent🎓Indian Institute of Technology DelhiNew Delhi · India2019–2024 · 4 papersEducation 2019–2024 · Bachelor and Masters, Computer Science and Engineering🎓Indian Institute of Information Technology DharwadDharwad · India2021–2025 · 2 papers🎓University of Colorado BoulderBoulder · United States2024 · 2 papers🎓Jaypee Institute of Information TechnologyNoida · India2014–2015 · 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 workComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1 workCode & programming languages · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning (2025 · 125 citations)
    Reasoning language models become more accurate when they generate longer chains of thought. However, users cannot set how many tokens a model spends, so they cannot plan compute budgets in advance. The authors present Length Controlled Policy Optimization (LCPO), a reinforcement learning recipe built on GRPO. Each…
  2. AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement (2024 · 11 citations)
    Code produced by large language models comes with no correctness guarantee, and the usual checks (unit tests, human review) are weak stand-ins.

All works

6 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. L1: Controlling How Long A Reasoning Model Thinks With Reinforcement LearningarXiv:2503.04697Pranjal Aggarwal, Sean Welleck2025 · 125 citations
  2. AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and TreefinementarXiv:2412.06176Pranjal Aggarwal, Bryan Parno, Sean Welleck2024 · 11 citations
  3. Gym-Anything: Turn any Software into an Agent Environment ↗arXiv (Cornell University) · no summary yetPranjal Aggarwal, Graham Neubig, Sean Welleck2026 · 5 citations
  4. On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists ↗arXiv:2605.20668 · no summary yetSeungone Kim et al.2026 · 2 citations
  5. Reasoning over mathematical objects: on-policy reward modeling and test time aggregation ↗arXiv (Cornell University) · no summary yetPranjal Aggarwal et al.2026 · 1 citation
  6. cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents ↗arXiv (Cornell University) · no summary yetPranjal Aggarwal et al.2026

 Co-authors here

1 co-author with a page, the most papers together first.

 Cited by

Papers of our library cite their works 137 times (of the 149,439 papers whose bibliographies we read); 62 citing papers with a page here →.

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