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Noveen Sachdeva

Noveen Sachdeva has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining, Reasoning, Efficient models & compression; often with Julian McAuley, Dan Zhang, Ed H. Chi.

3 papers in our library · 2023–2025

OpenAlex · ORCID · Wikidata · h-index 7 · 26 works · 270 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1,042 times in our library · ⭐ Paper Stars ranking: #2655

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

🏢Google DeepMindLondon · United Kingdom2024–2026 · 4 paperscurrent🏢GoogleMountain View · United States2026 · 1 papercurrent🎓University of California San DiegoSan Diego · United States2021–2024 · 7 papers🎓International Institute of Information Technology, HyderabadHyderabad · India2018–2020 · 4 papers

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).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 workEfficient models & compression · 1 work

 Works by year

2020s

20251 paper20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025 · 974 citations)
    This technical report from the Gemini Team at Google presents the Gemini 2.X family: Gemini 2.5 Pro, Gemini 2.5 Flash, and the earlier Gemini 2.0 Flash and Flash-Lite.
  2. How to Train Data-Efficient LLMs (2024 · 50 citations)
    Pre-training large language models consumes enormous amounts of data and compute, and scaling laws mean that simply adding more of either yields shrinking gains.
  3. Data Distillation: A Survey (2023 · 37 citations)
    Training large neural networks on ever-growing datasets is slow, costly and energy-hungry. This survey by Sachdeva and McAuley reviews data distillation: building a very small set of synthetic examples that can replace the full dataset for model training, architecture search and other procedures that repeatedly pass…

 Co-authors here

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

 Cited by

Papers of our library cite their works 1,042 times (of the 149,439 papers whose bibliographies we read); 396 citing papers with a page here →.

Updated

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