2026: The year of environments

Our NeurIPS 2025 retrospective

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.

The evolution we’ve witnessed

When we first brought Snorkel AI research to NeurIPS back in 2019, data-centric AI barely registered as a topic. Fast forward to 2025, and there’s an entire section of the conference floor dedicated to it. That kind of shift doesn’t happen by accident—it’s the result of countless researchers taking stock of the central role of top-quality data in realizing the best outcomes with AI.

What stood out this year

A few themes dominated the conversations we had and the talks we attended.

2026 will be the year of environments. Through talks like Aksel Joonas Reedi’s presentation on OpenEnv, Mike Merrill’s discussion of Terminal-Bench 2.0, and Grégoire Mialon’s discussion of ARE, we observed that the community is getting serious about building diverse, scalable environments for evaluations and RL. The insight that environments provide a natural curriculum for scaling complexity feels like it’s going to shape a lot of work in 2026. Noteworthy papers include:

• MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers—https://arxiv.org/abs/2508.20453
• SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks— https://arxiv.org/abs/2506.11791
• TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks— https://arxiv.org/abs/2412.14161
• DefenderBench: A Toolkit for Evaluating Language Agents in Cybersecurity Environments— https://arxiv.org/pdf/2506.00739

Data still needs human expertise. While tools and techniques are naturally vital, the trend that stands out is a greater recognition that data quality has a make-or-break impact on achieving desirable results, and working with human experts is still the best way to deliver top-quality data. We found some very interesting datasets among the accepted papers this year:

• HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages—https://arxiv.org/abs/2505.11475
• Sheetpedia: A 300K-Spreadsheet Corpus for Spreadsheet Intelligence and LLM Fine-Tuning—https://openreview.net/pdf?id=4vLYwlA3X5

Rubrics are getting more principled. We saw exciting work on more systematic factorization of evaluation criteria, new human-in-the-loop paradigms for data development, and frameworks for continual learning. In Liangchen Luo’s talk, How to Develop in the Agentic Era, the emphasis on building evals before training strongly reinforces the notion that well-written rubrics and evaluation criteria are of utmost importance. Two papers of note here:

• Measuring what Matters: Construct Validity in Large Language Model Benchmarks— https://arxiv.org/abs/2511.04703
• SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents—https://arxiv.org/abs/2505.20411

Our events

Snorkel Social

Snorkel AI cofounder and CEO Alex Ratner, cofounder and Chief Scientist Fred Sala, and the broader Snorkel research team hosted an intimate evening of whiskey, small bites, and research-driven conversation at The Whiskey House in San Diego. We’re so grateful for everyone who joined us!

SEA Workshop (sponsorship)

We want to thank the SEA (Scaling Environments for Agents) workshop organizers for an excellent day, with highly engaging invited talks, and poster sessions that drew a great deal of interest. We were pleased to sponsor this event, along with our other Diamond sponsor Inclusion AI, and Platinum sponsors Vmax and Sonic Jobs.

Award winners

Outstanding papers:

• GEM: A Gym for Agentic LLMs—https://arxiv.org/abs/2510.01051
• RPGBENCH: Evaluating Large Language Models as Role-Playing Game Engines—https://arxiv.org/abs/2502.00595

Outstanding posters:

• Go-Browse: Training Web Agents with Structured Exploration—https://arxiv.org/abs/2506.03533
• Scaling Open-Ended Reasoning to Predict the Future

Nikhil Chandak, Shashwat Goel, Ameya Prabhu, Moritz Hardt, Jonas Geiping

Thank you

To everyone who shared their work, challenged our thinking, and stopped by to chat—thank you. The progress in this field happens because researchers are willing to publish their failures alongside their successes, and build on each other’s ideas.

We’re heading into 2026 energized by what we saw. If the trends at NeurIPS are any indication, it’s going to be a big year for environments, evaluation, and data-centric approaches to AI development.See you at the next one. And in the meantime, if you’re interested in collaborating with us on building impactful environments or need expert-verified data developed in agent environments, come talk to us!