RL environments for faster, better learning
Guide your models to success on complex agentic tasks with real-time rewards and actionable insights.
Supercharge your RL objectives
Labelbox delivers high-fidelity RL environments and gyms with continuous observability, helping teams converge on reward signals faster and accelerate improvement across iterations.
We craft RL environments so agents act, interact, and receive programmatically verified outcomes, enabling tight feedback loops and reliable credit assignment.
Reward quality emerges from the coupling of task design, agent behavior, and evaluation, making co-evolution essential for efficient learning.
The Labelbox difference
When building an RL environment, the hardest question is also the most important: is this environment actually working? Traditional development often operates as a black box, with long feedback loops that leave teams discovering fragile task logic or reward misalignment only after an agent has already failed.
Real-time dashboards
showing agent performanceContinuous scoring
as environments evolveVisibility
into where tasks are too easy, too brittle, or misleadingHow it works
Design initial environment
Run agents through it
Visualize trajectories and failures
Identify shortcuts, edge behaviors, or blind spots
Refine tasks, tools, or verification
Re‑run and compare
Example RL environments built for real world complexity
Agentic & tool-use
Multi‑step task sequences
Tool calling, function execution, real‑world workflows
Standard structure per domain: database schema, task sequence (e.g., e‑commerce sale with promotions), tool calls, verification strategy
Code RL & judges
Rubric‑based or fully verifiable rewards
Constraint programming and advanced logic
Custom test cases that surface edge behavior and shortcuts
Code & software engineering
Private repos and SWEBench‑style tasks
Repo repair, container failures, real‑world bugs
Front‑end RL environments for long‑horizon coding