A world for the work
Tasks, tools, records, rules and consequences.
How World0 works
One workflow.
The whole RL cycle.
Build a world around the work. Establish a starting point, train from verified outcomes, then return to the task and measure what changed.
The full reinforcement learning cycle
Build. Evaluate.
Train. Retest.
A starting point
Run the agent and verify what it actually did.
Rewards into learning
Use scored experience to update the model.
Return to the work
Inspect the updated agent’s actions, successes and failures.
Connect your agent
to the world.
The environment exposes the tools and returns the consequences. This recorded finance exchange shows an agent requesting messages.
Choose the scope you need
The environment
For labs, environment suppliers and teams running their own training: tasks, tools, resets, rewards and evaluation records.
The full engagement
Scope a custom environment, evaluation, RL training and retesting around your model and workflow. The work and acceptance checks are agreed together.
Bring the work. Give it a world.
Define what your agent needs to do, the tools it uses, and what a correct outcome looks like.