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Alex Havrilla

Alex Havrilla has 2 papers in our library, in 2024, most of them on Language models & pretraining, Reasoning; often with Eric Hambro, Jane Dwivedi-Yu, Maksym Zhuravinskyi.

2 papers in our library · 2024

OpenAlex · h-index 4 · 15 works · 46 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 112 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7806

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

🎓Georgia Institute of TechnologyAtlanta · United States2021–2025 · 7 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 works

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Teaching Large Language Models to Reason with Reinforcement Learning (2024 · 61 citations)
    This study asks whether reinforcement learning techniques, popularised by RLHF, can strengthen the reasoning of large language models on math word problems.
  2. GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements (2024 · 45 citations)
    LLMs often fail to self-correct their reasoning without outside help. They cannot reliably tell whether a solution needs fixing or which step went wrong. This work splits refinement into three decisions: when, where, and how. It addresses them without human labels or stronger models, using only the ground-truth…

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. Teaching Large Language Models to Reason with Reinforcement LearningarXiv:2403.04642Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, et al.2024 · 61 citations
  2. GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local RefinementsarXiv:2402.10963Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.2024 · 45 citations
  3. Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models ↗arXiv:2412.02980 · no summary yetAlex Havrilla et al.2024 · 17 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 112 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

Updated

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