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Managed reinforcement learning

Train a model with evidence at every step.

Bring a qualified Taskset and a measurable baseline. OpenPond provisions the training infrastructure, runs bounded reinforcement learning, preserves the recipe and adapter, and puts evaluation before promotion.

Open Managed RLRead the training guide
OpenPond Managed RL showing model training metrics and evaluation results

Training starts after the task earns it.

Managed RL is the last mile of a continuous-improvement loop, not the first experiment. Compare the base model, inspect reward variation, confirm the frozen evaluation set, and approve a bounded run before GPU work begins.

  1. 01

    Qualify the Taskset

    Freeze the task definition, validation and evaluation splits, verifiers, graders, baseline, model profile, and spend boundary.

  2. 02

    Run bounded RL

    Provision managed GPU and rollout capacity, execute the approved GRPO or RFT recipe, and retain checkpoints with their lineage.

  3. 03

    Evaluate before promotion

    Compare the candidate against the frozen baseline, inspect failures and regressions, then explicitly promote, revise, or discard it.

A completed OpenPond Managed RL run with training and evaluation evidence

Portable by design

Keep the adapter and the complete recipe.

Every candidate stays connected to its source Taskset, model identity, trainer configuration, reward evidence, checkpoints, and evaluation outcome. Export the result or serve an approved version through OpenPond.

Managed infrastructure. Explicit decisions.

OpenPond handles the operational path while your team keeps control of the task, evaluation, budget, and promotion gate.

Qualified Tasksets with frozen validation and evaluation evidence
Managed rollout pools, GPU training, checkpoints, and cleanup
Bounded spend, failure handling, and inspectable run lineage
Independent evaluation, adapter export, serving, and promotion

Explore OpenPond.

DesktopWork with agents and train models beside your local projects.HarnessRun and inspect the complete learning loop from source.EnterpriseWork with our team to create and improve a custom model.