Nihal Nayak

I’m on the job market. Reach out if you’re interested in hiring me!

I am a Postdoctoral Fellow at Harvard University (SEAS), working with David Alvarez-Melis. I completed my Ph.D. in Computer Science at Brown University, advised by Stephen Bach.

I develop scalable, data-centric methods for building reliable frontier AI systems. My research studies zero-shot generalization: the ability of a system to solve new tasks without human annotations for those tasks. I develop methods to select and generate useful training data, train models efficiently, and audit their generalization failures.

My work spans three connected areas:

Principled data curation: Efficiently building the next generation of AI systems requires automated methods to select and generate high-quality training data. My recent work on targeted instruction selection advances this goal by identifying how to select training data that predictably improves performance on target tasks, while Bonito enables the synthesis of training datasets from unstructured expert documents.
Efficient and flexible models: In collaboration, I developed boomerang distillation, which creates families of models of different sizes from a single distillation run, without additional training for intermediate sizes.
Auditing generalization: I build evaluations that reveal fundamental limitations in foundation models. For example, our concept-binding benchmark shows that vision-language models such as CLIP struggle to associate attributes with the correct objects, failing to reliably distinguish a red cube and a yellow sphere from a yellow cube and a red sphere.

Additional Materials: Research Statement | Teaching Statement | Service Statement

news

Sep 25, 2026	Our work on understanding the role of layer patching in model size interpolation was accepted to NeurIPS 2026.
Sep 14, 2026	Invited talk at the University of Pennsylvania, CLunch.
Aug 24, 2026	Our work on ADAPT, which extends boomerang distillation across post-trained model families, was accepted to Findings of EMNLP 2026.

selected publications

ICML

A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn’t)

Nihal V. Nayak, Paula Rodriguez-Diaz, Neha Hulkund, Sara Beery, and David Alvarez-Melis

In International Conference on Machine Learning (ICML) 2026

Bib PDF Code

@inproceedings{nayak:icml26,
  title = {A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)},
  author = {Nayak, Nihal V. and Rodriguez-Diaz, Paula and Hulkund, Neha and Beery, Sara and Alvarez-Melis, David},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  abbr = {ICML},
  preprint = {false},
  bibtex_show = {true},
  selected = {true},
  pdf = {https://arxiv.org/abs/2602.14696},
  code = {https://github.com/dcml-lab/targeted-instruction-selection}
}

ICLR

Boomerang Distillation Enables Zero-Shot Model Size Interpolation

Sara Kangaslahti, Nihal V. Nayak, Jonathan Geuter, Marco Fumero, Francesco Locatello, and David Alvarez-Melis

In International Conference on Learning Representations (ICLR) 2026

Bib PDF Code

@inproceedings{kangaslahti:iclr26,
  title = {Boomerang Distillation Enables Zero-Shot Model Size Interpolation},
  author = {Kangaslahti, Sara and Nayak, Nihal V. and Geuter, Jonathan and Fumero, Marco and Locatello, Francesco and Alvarez-Melis, David},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  pdf = {https://arxiv.org/abs/2510.05064},
  code = {https://github.com/dcml-lab/boomerang-distillation},
  abbr = {ICLR},
  preprint = {false},
  bibtex_show = {true},
  selected = {true}
}

Findings

Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation

Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach

In Findings of the Association for Computational Linguistics: ACL 2024 2024

Bib PDF Blog Code Project

@inproceedings{bonito:aclfindings24,
  title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
  author = {Nayak, Nihal V. and Nan, Yiyang and Trost, Avi and Bach, Stephen H.},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2024},
  year = {2024},
  selected = {true},
  preprint = {false},
  bibtex_show = {true},
  abbr = {Findings},
  pdf = {https://arxiv.org/abs/2402.18334},
  blog = {https://snorkel.ai/how-bonito-helps-fine-tune-specialized-llms-faster-than-ever/},
  code = {https://github.com/BatsResearch/nayak-arxiv24-code},
  project = {https://github.com/BatsResearch/bonito}
}

ICLR

Learning to Compose Soft Prompts for Compositional Zero-Shot Learning

Nihal V. Nayak, Peilin Yu, and Stephen H. Bach

In International Conference on Learning Representations (ICLR) 2023

Bib PDF Code

@inproceedings{csp:iclr23,
  author = {Nayak, Nihal V. and Yu, Peilin and Bach, Stephen H.},
  title = {Learning to Compose Soft Prompts for Compositional Zero-Shot Learning},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  abbr = {ICLR},
  bibtex_show = {true},
  selected = {true},
  pdf = {https://arxiv.org/abs/2204.03574},
  code = {https://github.com/BatsResearch/csp}
}

ICLR

Multitask Prompted Training Enables Zero-Shot Task Generalization

Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush

In International Conference on Learning Representations (ICLR) 2022

Bib PDF Code Project

@inproceedings{sanh:iclr22,
  author = {Sanh, Victor and Webson, Albert and Raffel, Colin and Bach, Stephen H. and Sutawika, Lintang and Alyafeai, Zaid and Chaffin, Antoine and Stiegler, Arnaud and Scao, Teven Le and Raja, Arun and Dey, Manan and Bari, M Saiful and Xu, Canwen and Thakker, Urmish and Sharma, Shanya Sharma and Szczechla, Eliza and Kim, Taewoon and Chhablani, Gunjan and Nayak, Nihal V. and Datta, Debajyoti and Chang, Jonathan and Jiang, Mike Tian-Jian and Wang, Han and Manica, Matteo and Shen, Sheng and Yong, Zheng Xin and Pandey, Harshit and Bawden, Rachel and Wang, Thomas and Neeraj, Trishala and Rozen, Jos and Sharma, Abheesht and Santilli, Andrea and Fevry, Thibault and Fries, Jason Alan and Teehan, Ryan and Biderman, Stella and Gao, Leo and Bers, Tali and Wolf, Thomas and Rush, Alexander M.},
  title = {Multitask Prompted Training Enables Zero-Shot Task Generalization},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  pdf = {https://arxiv.org/abs/2110.08207},
  project = {https://github.com/bigscience-workshop/promptsource},
  code = {https://github.com/bigscience-workshop/t-zero},
  abbr = {ICLR},
  bibtex_show = {true},
  selected = {true}
}