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RL Envs
Dynamic, feedback-driven environments for domain-specific agent training and evaluation.
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From Benchmarking to Realistic RL
Patronus AI creates research-grade datasets and benchmarks tailored specifically for AI agents—capturing complex, real-world reasoning, decision-making, and multi-step workflows that generic data can't simulate. We’ve developed some of the most rigorous agent evaluation tools in the market.
1
FinanceBench
10,000+ expert-annotated Q&A pairs from real SEC filings for evaluating financial reasoning and compliance in advanced LLMs
2
BLUR
573 natural “tip-of-the-tongue” queries across text, sketches, audio, and languages, exposing memory and multimodal reasoning gaps in top agents
3
TRAIL
Benchmark for agentic reasoning and trace evaluation with 20+ failure types and human-labeled execution paths; SOTA models score <11%
4
MemTrack
Tests long-term memory and retrieval in LLM agents, tracking context retention and consistency across complex, multi-step reasoning tasks.
RL Environments Catalog
Our Differentiators
Ecologically valid and human-centric interruptions
We provide realistic interruptions that would occur in various real-life settings such as pop-ups and advertisements in our Computer Use envs and reprioritization requests and breaks in our Coding envs.
Configurable difficulty levels
The tasks for each environment can come with configurable difficulty levels. Often, this is defined by the level of ambiguity of the task or the number of available distractions that can be introduced in the environment.
Multi-agent environments
We also create environments with multi-agent set-ups to simulate real-world interactions, such as a user interacting with a customer service representative and teammates interacting on a product development team to ship a new update.
Self-play and exploration-driven
Our environments also encourage agent self-play and exploration to increase determinism in the setting (better prediction of agent behavior) and develop more experience through the creation of new perspectives/roles.