Vibrant Labs
Vibrant Labs provides autoscaled reinforcement learning data for frontier AI agents through unsupervised environment design, automatically generating novel training challenges without human annotation. The platform supports both tool-use and computer-use modalities, enabling agents to train on API chains and real UI navigation across ITSM, healthcare, finance, and customer support workflows. Its open-endedness architecture creates self-evolving benchmarks that adapt to agent progress, with a pipeline supporting 14,000+ evaluations and 1 billion tokens processed.
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Frontier AI agents require large volumes of diverse, high-quality reinforcement learning data for post-training, but generating this data typically depends on extensive human annotation and manual benchmark curation. Human-curated benchmarks saturate quickly, limiting an agent's ability to encounter novel challenges and continue learning beyond the initial training data.
Solution
Vibrant Labs provides autoscaled reinforcement learning data for frontier agents through unsupervised environment design, which automatically generates novel and learnable challenges without human annotation. The platform supports both tool-use and computer-use modalities, enabling agents to train on API chains, real UI navigation, and complex workflows across sectors such as ITSM, healthcare, finance, and customer support. By continuously producing new environments, the system creates self-evolving benchmarks that keep pace with agent capabilities, ensuring agents never run out of things to learn. This approach reduces reliance on static datasets and human-in-the-loop annotation, enabling continuous agent improvement at scale.
Target Audience
Primary customers are AI research teams and enterprises developing frontier agent systems that require continuous post-training, including those building tool-use and computer-use agents for enterprise workflows.
Features
- Unsupervised environment design that automatically generates training challenges without human annotation
- Tool-use modality supporting agents that call APIs and chain tools across ITSM, healthcare, finance, and customer support workflows
- Computer-use modality for agents that navigate real user interfaces end-to-end, including e-commerce, web research, and SaaS workflows
- Open-endedness architecture designed to generate novel, learnable challenges rather than saturating human-curated benchmarks
- Self-evolving benchmarks that adapt to agent progress and prevent learning plateaus
- Scalable RL data pipeline supporting 14,000+ evaluations with 1 billion tokens processed