TL;DR: We built FinQA — a financial question-answering environment with 290 expert-curated questions across 22 public companies, now available on OpenEnv. Agents use MCP tools to discover schemas, write constrained SQL queries, and answer multi-step questions from real SEC 10-K filings. Most open-source models struggle with this kind of multi-step tool use, and even frontier closed-source models, while more accurate,…
The Snorkel research team collaborated with the rLLM team at UC Berkeley on the Agentica project, using their open-source rLLM framework to fine-tune Qwen3-4B-Instruct-2507, delivering a model that beats Qwen3-235B-A22B on Snorkel AI’s expert-curated financial benchmarks – at 1/60th the size. A full breakdown of the results are published in the rLLM blog here. The key insight? Just focus on…
Error analysis of 8 models on Agentic Coding tasks Successful completion of complex tasks doesn’t come from models being always right. It comes from models being resilient when things go wrong. To get a deeper understanding of model behavior in agentic environments, our team analyzed all of the errors found in the full traces of tasks from our Agentic Coding…
Today, AI is marked by a growing asymmetry: the excitement around agentic AI is real — backed by quantitative progress on model cards and genuine leaps forward, especially in coding. But ask individuals or enterprises where they feel ready to deploy agentic automation in high-stakes, domain-specific settings outside of coding… and you will find hesitation. The reason: our ability to…
SlopCodeBench reveals how AI coding agents degrade code quality over time—measuring “slop,” technical debt, and architectural erosion across iterations.
Today, we’re sharing details about the Snorkel Agentic Coding benchmark—a comprehensive evaluation suite designed to test whether agents can handle the full complexity of software engineering work.
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…
