Reinforcement Learning Environments for AI Agents | Scale AI

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RL Environments

Train and evaluate agents to excel at long-horizon, professional workflows.

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Features

Made for Agent Training

Real Applications, Rebuilt for RL

Simulated APIs, MCP servers, and GUIs that behave like the systems agents actually use.

Expert-Curated Artifacts

Files (PDFs, CSVs, PPTs, etc.), data, and state settings that reflect real professional workflows, sourced from real business settings, capturing natural messiness.

Hard Tasks with Verifiable Outcomes

Expert-designed objectives, rubrics, and automated verifiers that produce stable training signals for programmatic process and outcome evaluation.

Easy Integration and Environment Control

Compatible with standard environment interfaces, supporting trajectories, resetting state, examining rewards, and more.

Overview

Inside Scale AI's RL Environments

Spreadsheet-based RL environment for AI agent training.

Built to Advance Agent Capabilities

Scale AI's RL Environments are simulated collections of realistic applications designed to train and evaluate agent behavior.

RL environment modeling developer tools and APIs.

Systems for Stronger Learning Signals

They mirror real computer and API-based systems, supporting rich logs and application state that can be used for programmatic and rubric-based evaluation.

RL environment simulating file management and document systems.

RL-Ready Expert Data

Each environment combines simulated interfaces with expert-curated data and evaluators to produce reliable learning signals. Scale also provides analysis on complexity (pass@k) and anticipated training gains.

Capabilities