> arena: post-training for open models

Your task.
Mastered by your agent.

Arena turns your raw data into agents, specialized at your task. Skip the reliance on someone else's frontier model.

Trusted by teams at

NVIDIA IBM Roblox Decision Lab Intropic RTDynamics

> platform

Build, train and ship agents with Arena
Powered by reinforcement learning

Train on your tasks

Turn your workflows into training environments and let agents learn them directly.

Reward 0.00 step 0

Automatic tuning

Our evolutionary hyperparameter optimization and async-RL engine trains your agents 10x faster.

generation 0best 0.00

LLM fine-tuning

Reinforcement fine-tuning for LLM agents, with distributed training and one-click deployment.

> user reward 0.00 step 0

Validate before you spend

Check your dataset or environment upfront and start training with full confidence.

validating dataset… ready to train

> how it works

From raw data to a deployed agent

01Connect & validate

Bring your data.

  • Use Arena to prepare it as LLM datasets and RL environments.
  • Validate everything with feedback before training begins.
Arena dataset screen with question and answer columns mapped on a customer support ticket triage datasetArena CLI validating an environment

02Ready for launch

Configure everything.

  • Select algorithms, rewards, constraints and objectives.
  • Enable evolutionary tuning to explore promising configurations automatically.
Arena experiment setup with GRPO selected on Qwen3-4B and LoRA parametersArena CLI submitting an experiment

03Built for scale

Train across your whole compute stack.

  • An optimized async-RL engine with distributed training.
  • Squeeze all the performance out of your spend.
  • Monitor metrics, sample efficiency and checkpoints in real time.
Arena results comparing an evolutionary HPO run against a baseline on evaluation score and training scoreArena CLI streaming experiment metrics

04Deploy & improve

Ship it, then keep it learning.

  • One-click promote to production, securely hosted on your infrastructure.
  • Select checkpoints, track performance and benchmark against baselines.
  • Continual learning on live feedback.
Arena deployed ticket triage agent routing support messages in the chat playgroundArena CLI deploying an agent

> why arena

The smarter way to build agents

What changes when you train a specialist on your own task instead of renting a general model.

Standard approaches
AgileRL Arena
Quality
Frontier models are generally good, but aren't specialized at what matters to you.
Small models fine-tuned on your task become experts.
Learning
Agents stop learning after training and deployment.
Agents keep learning. Live results feed the next run.
Security
Hosted fine-tuning and models carry a supply chain and data security risk.
Training runs in your environment, and the model weights remain yours.
Speed
Costly and time-consuming training runs.
10x faster training with automated evolutionary tuning.
Cost
Tied to one frontier model provider, with costs that scale with usage.
Use any open-source model, served on your own infrastructure.

> security

Your data. Your weights. Your cloud.

Enterprise teams use Arena because nothing has to leave their perimeter.

Visit the Trust Center →

01

Training runs in your environment

Bring your own cloud or use ours. Your datasets stay where they are.

02

The weights are yours

Every model you train belongs to you, and you can serve it on your own infrastructure.

03

SOC 2 Type II and ISO 27001 certified

SOC 2 Type II badge
SOC 2 Type II
Trust Services Criteria
ISO 27001 badge
ISO 27001
Information security management

> import agilerl

The framework Arena runs on.
Open-source.

AgileRL is the reinforcement learning library under Arena. Evolutionary hyperparameter optimization tunes a whole population while it trains, so you skip the grid search.

Lunar Lander benchmark

10x faster
AgileRLLeading HPO library
  • Open-source

    Open-source framework with docs, examples, and community support

  • Single and multi-agent

    Single and multi-agent support across on/off-policy, offline RL, bandit and LLM training

  • Python-first

    Python-first, compatible with your data and environments

  • Scales to multi-GPU

    Works with your cloud compute and scales to multi-GPU

10x faster

Training with open-source v2

MIT CSAIL

Used by leading research labs and institutions

450,000+

Downloads from the community

10x faster: AgileRL evolutionary HPO vs. a leading hyperparameter optimization library on the Lunar Lander benchmark shown above.

Watch your agent master your task, live

Bring a task, dataset, or environment. We'll train, tune, and deploy an agent on it with you in a live session.

Book a demo
Arena home dashboard showing a live evolutionary HPO training run, recent experiments and deployed agents