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Iman Mirzadeh

Iman Mirzadeh has 2 papers in our library, in 2024, most of them on Language models & pretraining, Reasoning; often with Mehrdad Farajtabar, Keivan Alizadeh, Minsik Cho.

2 papers in our library · 2024

OpenAlex · h-index 8 · 25 works · 395 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 299 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5096

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

🏢AppleCupertino · United States2024–2025 · 4 papers🎓KTH Royal Institute of TechnologyStockholm · Sweden2012–2013 · 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputational Theory and Mathematics · 1 workMathematics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 work

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models (2024 · 273 citations)
    Scores on GSM8K, the standard grade-school math benchmark, keep rising. The authors ask whether that reflects real reasoning or just familiarity with a fixed and possibly leaked test set. They build GSM-Symbolic by turning GSM8K problems into templates, where names, numbers and family terms become variables with…
  2. Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization (2024 · 8 citations)
    Training large language models from random weights is slow and costly, while small models are cheap but less accurate.

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. GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language ModelsarXiv:2410.05229Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, et al.2024 · 273 citations
  2. The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity ↗arXiv:2506.06941 · no summary yetParshin Shojaee et al.2025 · 104 citations
  3. Scaling Smart: Accelerating Large Language Model Pre-training with Small Model InitializationarXiv:2409.12903Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh Vahid, et al.2024 · 8 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 299 times (of the 149,439 papers whose bibliographies we read); 49 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/iman-mirzadeh/