🧑‍🔬 Daman Arora

🏛️ Affiliations

🎓Carnegie Mellon UniversityPittsburgh · United States2025–2026 · 5 paperscurrent🏢Microsoft ResearchBengaluru · India2023–2024 · 2 papers🎓Indian Institute of Technology DelhiNew Delhi · India2023 · 2 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.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models (2023 · 107 citations)
   As LLMs approach saturation on reasoning benchmarks such as GSM-8K, MATH and MMLU, the authors propose a harder test.
2. Training Language Models to Reason Efficiently (2025 · 74 citations)
   Large reasoning models solve hard problems by producing very long chains of thought. This makes serving them expensive and slow, because attention cost grows quadratically and the KV cache grows linearly with length. Arora and Zanette propose teaching such models to reach correct answers with fewer tokens. They use…
3. Learning and Leveraging Verifiers to Improve Planning Capabilities of Pre-trained Language Models (2023 · 10 citations)
   This paper examines why fine-tuned language models plan poorly. In the Blocksworld domain, the authors find that the main failure is proposing actions whose preconditions do not hold in the current state. They fine-tune GPT-2 as a generator that reads a textual goal and current state and outputs the next action and…

All works

6 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. Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Daman Arora, Himanshu Singh, Mausam2023 · 107 citations
2. Training Language Models to Reason EfficientlyConference on Neural Information Processing Systems (NeurIPS)Daman Arora, Andrea Zanette2025 · 74 citations
3. Learning and Leveraging Verifiers to Improve Planning Capabilities of Pre-trained Language ModelsarXiv:2305.17077Daman Arora, Subbarao Kambhampati2023 · 10 citations
4. Maximum Likelihood Reinforcement Learning ↗arXiv (Cornell University) · no summary yetFahim Tajwar et al.2026 · 4 citations
5. Tail-Likelihood Reinforcement Learning ↗arXiv (Cornell University) · no summary yetShrinivas Ramasubramanian et al.2026 · 1 citation
6. Harness Learning Enables Generalizable Test-Time Adaptation ↗arXiv (Cornell University) · no summary yetAlvin Zhang et al.2026

🤝 Co-authors here

3 co-authors with a page, the most papers together first.

• Himanshu Singh
• Mausam
• Subbarao Kambhampati

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2023
• Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2025

🔗 Cited by

Papers of our library cite their works 140 times (of the 149,439 papers whose bibliographies we read); 56 citing papers with a page here →.

Updated 2 November 2026

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