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We just returned from NeurIPS 2025, and we’re still processing everything we saw. The energy around data-centric AI has never been stronger—and we couldn’t be more grateful to the research community for pushing these ideas forward.

Explores how rubrics support agentic, multi-turn, tool-using, multimodal, and code-generating AI systems, and how they evolve with AI feedback and ensemble evaluation.

TL;DR: We stress-tested the “generate → criticize → improve” loop on 50 visual reasoning tasks. The results were counterintuitive: self-critique acts as a corrosive agent on high-performance tasks, turning 98% accuracy into 57%. Yet, for tasks where models fail completely, it works like magic. This difficulty-dependent behavior poses a critical, hidden risk for RLFT pipelines. The promise vs. the reality…

Snorkel Chief Scientist Fred Sala and Kobie Crawford chat with the Terminal-Bench team to unpack the design behind Terminal-Bench 2.0 and the new Harbor framework.

Snorkel AI contributes specialized datasets to Hazy Research’s “Intelligence-per-Watt” study, advancing how efficiently AI turns energy into intelligence.

Terminal-Bench 2.0 launches today, marking a major leap in AI agent evaluation. Snorkel AI contributed key research and task design to this release.

We unpack what makes a high-quality RL environment for LLMs and show how we build realistic, enterprise-grade environments at Snorkel AI.

A procedurally generated and programmatically verified benchmark for evaluating spatial reasoning capabilities in LLMs Large language models (LLMs) are showing remarkable results on solving complex reasoning problems across domains—from mathematical proofs and logical puzzles to graduate-level science and engineering questions. On the other hand, their spatial reasoning capabilities are less understood, even though such reasoning underlies many everyday tasks. We…

Snorkel’s “Trusted Scale” philosophy Welcome to Part 4 of Snorkel AI’s rubric series. In previous posts, we explored how rubrics enable structured evaluation (Part 1), the spectrum of rubric types and use cases (Part 2), and the science behind designing and validating them (Part 3). In this latest installment, we pull back the curtain on how Snorkel puts these principles…