AgileRL raises $7.5M to take Reinforcement Learning out of the research lab and into the enterprise

Reinforcement Learning (RL) has long been the “gold standard” of the artificial intelligence world. While supervised learning powers your Netflix recommendations and photo tagging, RL is the engine behind the high-stakes world of autonomous vehicles, high-frequency trading, and the “RLHF” (Reinforcement Learning from Human Feedback) that makes Large Language Models actually helpful.

The problem? Until now, doing RL at scale required a small army of Ph.D.s and a budget that would make a CFO faint.

AgileRL, a London-born startup, wants to change that. The company announced today that it has raised $7.5 million in Seed funding to productize the RL workflow. The round was led by Fusion Fund, with participation from Flying Fish, Octopus Ventures, Entrepreneur First, and Counterview Capital.

The “Ph.D. Bottleneck”

To understand why AgileRL is gaining traction, you have to understand the current state of RL development. In most enterprises, building an RL program is less like software engineering and more like running a high-end AI lab.

“Having built a reinforcement learning system from scratch at my last company, I saw firsthand how costly and complex it is,” said Param Kumar, co-founder of AgileRL. “Every new use case breaks the old setup. You’re constantly rebuilding simulators, reward designs, and deployment pipelines from scratch.”

Because RL agents learn through trial and error in a simulated environment, the “hyperparameters”, the settings that govern how they learn, are notoriously finicky. A single wrong setting can lead to a model that fails to learn anything at all, wasting weeks of compute time.

Evolutionary Gains

AgileRL’s secret sauce is its move away from manual tuning toward Evolutionary Hyperparameter Optimization.

Instead of training one agent and hoping the settings are right, AgileRL’s platform, Arena, trains a population of agents simultaneously. The platform identifies the “strongest” performers, evolves their traits, and discards the “weak” ones in real-time.

This approach yields several key benefits:

• 10x Speedup: By automating the optimization process, companies can reach production-ready models in a fraction of the time.
• Reduced Compute Costs: Evolutionary training prevents “dead-end” runs that burn GPU hours without results.
• RLOps for Everyone: Arena provides an end-to-end “RLOps” pipeline, covering everything from environment validation to one-click deployment.

The market is clearly hungry for a standardized toolkit. AgileRL’s open-source framework has already seen more than 300,000 downloads, with engineers at JPMorgan, Wayve, IBM, and Huawei putting the tech through its paces.

From London to San Francisco

While the company started in the U.K. ecosystem via Entrepreneur First, the new capital is earmarked for a heavy push into the U.S. market. AgileRL plans to open a San Francisco office and hire a dozen new roles across engineering and go-to-market teams to capture the growing demand for RL in robotics and defense.

“Reinforcement learning remains the gold standard of AI training, yet very few companies actually have the resources to implement it in-house,” said Lu Zhang, Founder and Managing Partner at Fusion Fund. “As companies move beyond simple chatbots and into complex autonomous systems, AgileRL provides the infrastructure they’ve been missing.”

In a 2026 landscape where “AI” is no longer a buzzword but a core utility, AgileRL is betting that the companies that win won’t just be the ones with the most data, they’ll be the ones who can train their agents the fastest.

Companies can try AgileRL today at: https://www.agilerl.com/.