Mocked environments hide the failures that surface when agents touch real dependencies, tests, latency, and permissions. Runloop gives post-training teams one execution layer for three workloads: RL rollouts, SFT data generation, and verified trajectory generation. Each rollout runs in an isolated sandbox provisioned from a reproducible Blueprint, executes against production-like dependencies, and returns structured outcomes your training pipeline can consume.
Provision from pre-warmed Blueprints so environment startup isn't the bottleneck in latency-sensitive loops. For many workloads, round-trip time is dominated by model generation and test execution rather than sandbox spin-up.
Pin an immutable Blueprint so every rollout in a batch starts from the same environment, dependencies, permissions, and seed state.
Every rollout returns its terminal state and failure reason, so you can filter infrastructure failures out of your training labels instead of scoring them as model failures.
Runloop executes each rollout in an isolated sandbox and returns execution-verified outcomes from the validator suite, with sub-second provisioning from warm Blueprints. The training loop learns from production-like environments instead of stubs.
Runloop runs thousands of concurrent sessions to generate verified (task, trajectory, outcome) records in hours. Outcomes are labeled by execution rather than annotators, and failure trajectories become training data you can inspect, filter, and feed back into SFT or RL pipelines.
Runloop gives teams the execution layer for post-training, so they don't have to build and maintain their own sandbox fleet, environment versioning, credential scoping, and rollout infrastructure. One layer serves RL, SFT, and trajectory generation across multiple models and customer-specific environments.
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© 2025 Runloop AI, Inc.
© 2026 Runloop AI, Inc.
