RunRL is a platform that offers reinforcement learning as a service to improve large language models (LLMs) and AI agents. Their technology enables users to train, fine-tune, and optimize AI models through reinforcement learning, making AI agents more reliable and specialized for specific tasks.
How does RunRL work?
RunRL addresses the challenge of unreliable, generic LLM agents by providing RL fine-tuning. Users can define a reward function to specify what behavior is considered 'good' or 'bad,' and RunRL's platform then applies reinforcement learning algorithms to optimize the model's performance accordingly. Key features include:
- AgentFlow: Enables AI agents to self-improve based on custom criteria.
- RL Fine-Tuning: Produces specialist models tailored to particular use cases.
- Integration: Supports integration with existing tech stacks for enterprise needs.
What are RunRL's primary applications?
RunRL's technology is applied across several domains, including:
- Chemistry (e.g., generating novel drug candidates)
- Web agents (for automating online tasks)
- Code generation (improving software development productivity)
- Voice agents (enhancing conversational AI systems)
They also offer specialized enterprise solutions, including custom reward development and access to RL expertise.
Who uses RunRL?
While specific customer names are not publicly listed, RunRL is designed for organizations and teams looking to deploy more reliable, high-performing AI agents—especially those needing custom behaviors or specialized models in sectors like biotech, software, and automation.
How was RunRL started?
RunRL was founded in 2025 by Andrew Gritsevskiy and Derik Kauffman in San Francisco, California. Both founders previously ran an AI research lab, and Andrew has a background in reinforcement learning research, having left his PhD to co-found RunRL. Their combined expertise underpins the platform's technical approach.
What sets RunRL apart?
RunRL distinguishes itself by making advanced reinforcement learning accessible as a service, reducing the barrier for organizations to produce specialist, reliable AI agents tailored to their unique requirements. The platform's flexibility in defining reward functions and integrating with existing infrastructure is a key differentiator.
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