Build, train, and deploy RL models at lightning speed
Arena unifies training, tuning, and deployment, allowing teams to ship production agents faster
Trusted by leading teams in research, gaming, finance, robotics and logistics.
Arena unifies training, tuning, and deployment, allowing teams to ship production agents faster
Trusted by leading teams in research, gaming, finance, robotics and logistics.
Train and deploy reinforcement learning agents faster than ever with Arena's SOTA tooling.
Evolutionary hyperparameter optimization and distributed training on any single- or multi-agent task.
Fine-tune LLMs using evolutionary HPO and one-click deployment. Train custom models on your data without infrastructure hassle.
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Validate your dataset or environment before training to ensure everything runs as intended.
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RL stacks sprawled across notebooks,
scripts, and bespoke infrastructure
Hyperparameter search is slow
and ad-hoc
Environment mismatches derail runs late into the pipeline
Shipping trained policies into production is
brittle and manual
A single platform to train, evaluate, and deploy
Built-in evolutionary tuning to converge faster with better results
Pre-flight environment validation before you spend
Hosted, one-click deployment for live inference at scale
Upload your LLM dataset or Python environment; validate your environment before training begins to ensure runs don’t fail hours in.
Select algorithms, rewards, constraints, and objectives; enable evolutionary tuning to explore promising configurations automatically.
Distribute across available GPUs/ instances; monitor metrics, sample efficiency, and checkpoints in real time.
One-click promote to production; track performance, roll back, or iterate with easy-to-buy training credits.
Enabling breakthrough results in training AI agents for complex aerial interception missions with RTDynamics
READ CASE STUDYSubstantially cutting compute expenses and boosting training speed for RL workflows with Warburg AI
READ CASE STUDYDramatically increasing utilisation and reducing training time for complex bin-packing with Decision Lab
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Python-first, compatible with custom environments
Single, and multi-agent support across on/off-policy, offline RL, and bandits
Works with your cloud compute and scales to multi-GPU
Open-source framework with docs, examples, and community support
Training with opensource V2
Used by leading research
labs and institutions
Downloads from the community
Top up training credits anytime - no plan change required. Get credits.
Built-in evolutionary HPO for smarter, faster training.
One-click deployment from experiment to production.
Distributed training at scale with multi-GPU support.
Reinforcement fine-tuning for LLMs.
Bring a sample environment or dataset and we will walk you through training, tuning, and deployment in a live session tailored to your use case.
Book a demo
