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Ido Hakimi

Ido Hakimi has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Alejandro Hernández-Cano, Alexander Hägele, Angelika Romanou.

2 papers in our library · 2026

OpenAlex · h-index 3 · 18 works · 40 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 156 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7223

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

🎓ETH ZurichZurich · Switzerland2025–2026 · 11 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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 worksInformation Systems · 1 work

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

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Reinforcement Learning via Self-Distillation (2026 · 74 citations)
    Policy-gradient RL with verifiable rewards, such as GRPO, gets only one scalar score per attempt. That makes it hard to tell which tokens caused a failure, and learning stops when every rollout in a group gets the same score. The authors point out that many environments also return text explaining the outcome, such…
  2. Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026 · 68 citations)
    Most "open" LLMs release only their weights. Their training data stays undisclosed, may ignore content owners' rights, and is heavily skewed toward English and a few high-resource languages. Apertus responds with a fully open suite of 8B and 70B models, both base and Instruct. Pretraining used 15T tokens drawn only…

All works

4 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. Reinforcement Learning via Self-DistillationarXiv:2601.20802Jonas Hubotter, Frederike Lubeck, Lejs Behric, et al.2026 · 74 citations
  2. Apertus: Democratizing Open and Compliant LLMs for Global Language EnvironmentsAnnual Meeting of the Association for Computational Linguistics (ACL)Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, et al.2026 · 68 citations
  3. MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors ↗Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · no summary yetJakub Macina et al.2025 · 33 citations
  4. From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning ↗Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · no summary yetDavid Dinucu-Jianu et al.2025 · 19 citations

 Co-authors here

6 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 156 times (of the 149,439 papers whose bibliographies we read); 31 citing papers with a page here →.

Updated

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