Param Kumar

CEO at AgileRL - Build, train, and deploy AI agents at lightning speed

[Param Kumar @kumar_param927]

We're giving engineering teams at enterprises $20,000 to train and deploy their own model on Arena.

If your ML team has a task it wants a specialised model for, apply here and we'll get back to you within a week: https://t.co/YNyr2qOBXk

[Fusion Fund @FusionFundVC]

Featured in @JensenHuang's @NVIDIA GTC keynote, our portco @AgileRL_Inc outperformed five open-source RL frameworks with Arena. Its model came within 1.3 points of @AnthropicAI's Claude Opus 5.5, showing how smaller #AI models can compete.

Congrats to @kumar_param927 & team!

[AgileRL @AgileRL_Inc]

We are proud to announce a collaboration between AgileRL and @NVIDIAAI to support the post-training of Nemotron models. NVIDIA's Nemotron open-source model family is now available for fine-tuning on Arena, our platform for creating AI agents specialized at any task.

NVIDIA gave us early access to their latest 30B3A parameter MoE model, Nemotron 3.5 Lightning. On Arena, we trained it to outperform Claude Sonnet 5 on two different tasks: a long-horizon reasoning challenge and a real customer support workload. Nemotron proved exceptionally easy to post-train: a single six-hour run on four H100 GPUs was enough to master the reasoning task, at context lengths beyond 50,000 tokens. The full results are in the announcement, linked below.

With this collaboration, Arena customers get access to NVIDIA Nemotron models as soon as they are released, with the training and evaluation setup already built around them. Post-train Nemotron on your own data, environment and edge cases, to build an agent that masters your task.

Across industries including banking, insurance, aerospace and government, production traffic is moving off frontier model APIs and onto infrastructure these companies control. Businesses want agents with genuine expertise in their specific task, trained on their own data. Security, control and data sovereignty demand that model weights remain on their own infrastructure. And they want the fixed cost of hardware they own, rather than per-token pricing that compounds with every request.

This collaboration provides that path. Begin with a dataset, an RL environment, or simply a description of the job the agent has to do, and our team will build the rest with you. Arena handles the training and deploys the finished agent in one click, with the weights yours to keep.