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Maksym Zhuravinskyi

Maksym Zhuravinskyi has 4 papers in our library, in 2024, most of them on Language models & pretraining, Reasoning, Semitic languages; often with Duy Phung, Jonathan Tow, Marco Bellagente.

4 papers in our library · 2024

OpenAlex · h-index 3 · 12 works · 31 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 152 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6731

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

 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).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 workComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 workSemitic languages · 1 work

 Works by year

2020s

20244 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Stable LM 2 1.6B Technical Report (2024 · 46 citations)
    Many strong language models are released without their training data or full recipe, which makes independent replication nearly impossible.
  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…
  3. Stable Code Technical Report (2024 · 9 citations)
    Code assistants such as autocomplete tools are called constantly, so a model compact enough to run locally on consumer hardware avoids network delays and dependence on gated APIs.
  4. Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic (2024 · 6 citations)
    Most Arabic-focused LLMs have at least 7B parameters, which makes them costly to run and slow at inference.

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Teaching Large Language Models to Reason with Reinforcement Learning ↗arXiv:2403.04642 · no summary yetAlex Havrilla et al.2024 · 61 citations
  2. Stable LM 2 1.6B Technical ReportarXiv:2402.17834Marco Bellagente, Jonathan Tow, Dakota Mahan, et al.2024 · 46 citations
  3. 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
  4. Stable Code Technical ReportarXiv:2404.01226Nikhil Pinnaparaju, Reshinth Adithyan, Duy Phung, et al.2024 · 9 citations
  5. Arabic Stable LM: Adapting Stable LM 2 1.6B to ArabicarXiv:2412.04277Zaid Alyafeai, Michael Pieler, Hannah Teufel, et al.2024 · 6 citations

 Co-authors here

12 of 16 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 152 times (of the 149,439 papers whose bibliographies we read); 34 citing papers with a page here →.

Updated

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