We develop methods, benchmarks, and training systems that turn expert data into frontier AI
building benchmarks and collaborating with
Featured research
Senior SWE-bench built by Snorkel with Princeton University and UW-Madison
Continual Learning Bench: Evaluating agents that adapt and improve over time
Learning from Less: Measuring the Effectiveness of RLVR in Low Data Compute Regimes
Agents' Last Exam: Challenge and measure AI agents on economically valuable and real-world tasks
SlopCodeBench: A community benchmark measuring code erosion
Harvey’s BigLaw Bench: Research
Terminal-Bench 2.0: Raising the bar for AI agent evaluation
Benchmarking Agents in Insurance Underwriting Environments
Agentic Coding benchmark: Evaluating AI Models on complex, real-world coding tasks
Vision and impact
We help labs advance frontier models by working with domain experts to design and build complex, realistic datasets that drive model performance.
Benchmarking & Evaluation
Build benchmarks that define and advance the AI frontier
Scaling Subject Matter Expertise
Define how subject matter experts encode their knowledge into data
RL, Training, & Data Valuation
Drive dataset development based on feedback from RL and model training
Community and open science
Open benchmarks, conversations, and research for real-world AI performance.
Open Benchmarks Grants
Backed by a $3M commitment, the program funds open-source datasets, benchmarks, and evaluation artifacts that shape how frontier AI systems are built and evaluated.
Benchtalks
Reading Group
DEEP RESEARCH Expertise
Technical advisors and distinguished affiliates
Browse research blogs and academic papers
In many practical few-shot learning problems, even though labeled examples are scarce, there are abundant auxiliary datasets that potentially contain useful information. We propose the problem of extended few-shot learning to study these scenarios. We then introduce a framework to address the challenges of efficiently selecting and effectively using auxiliary data in few-shot image classification. Given a large auxiliary dataset…
Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such…
Machine Learning Whiteboard (MLW) Open-source Series Our machine learning whiteboard (MLW) is an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in discovering more about machine learning.In this episode, Hiromu Hota, Vincent Sunn Chen, Daniel Y. Fu, and Frederic Sala dive…
In situations where explanations of black-box models may be useful, the fairness of the blackbox is also often a relevant concern. However, the link between the fairness of the black-box model and the behavior of explanations for the black-box is unclear. We focus on explanations applied to tabular datasets, suggesting that explanations do not necessarily preserve the fairness properties of…
In this episode of Science Talks, Snorkel AI’s Braden Hancock chats with Jason Fries – a research scientist at Stanford University’s Biomedical Informatics Research lab and Snorkel Research, and one of the first contributors to the Snorkel open-source library. We discuss Jason’s path into machine learning, empowering doctors and scientists with weak supervision, and utilizing organizational resources in biomedical applications of Snorkel. This episode is part…
The human ability to solve complex manipulation tasks is based on a flexible generalizable understanding of intuitive physics mostly learned through curiosity-driven self-play during infancy. We aim to replicate such interactive learning in artificial agents to achieve the same flexibility and generalizability when solving complex manipulation tasks. For that purpose, we introduce a general framework for learning intuitive physics through…
Machine Learning Whiteboard (MLW) Open-source Series Earlier this year, we started our machine learning whiteboard (MLW) series, an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in learning about machine learning.In this episode, our Co-founder and Head of Technology. Braden Hancock…
In this episode of Science Talks, Frederic Sala – an assistant professor of Computer Science at the University of Wisconsin Madison and a research scientist at Snorkel discusses his path into machine learning, the central thesis that ties together his multidisciplinary research, his thoughts on the future of weak supervision, as well as his decision to go into academia.
This paper presents a rigorous approach for using a set of arbitrarily correlated weak supervision sources in order to solve a multiclass classification task when only a very small set of labeled data is available
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