---

name: learning-pipelines description: "Guides RoboVerse reinforcement learning, imitation learning, VLA evaluation, fusion, dataset, checkpoint, and policy pipeline decisions." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Learning Pipelines

Use this route for training or evaluating RL, imitation-learning, VLA, or fusion policies; preparing an IL dataset; selecting a runner; loading a checkpoint; or debugging normalization and policy/environment contracts.

Route

1. Select the family in workflows.md: CleanRL, FastTD3, SB3, RSL-RL, IL runner/policy, VLA adapter, or fusion.
2. Install only the required extras. learn covers many learning dependencies; vla adds much larger TensorFlow/LeRobot/Transformers stacks. Individual policy folders can still have stricter versions.
3. Validate task registration, observation/action shapes, dataset schema, and a tiny CPU forward pass before allocating GPUs or starting data downloads.
4. Read data-and-checkpoints.md before conversion, resume, or evaluation; checkpoint normalization state and config are part of the policy contract.
5. Use troubleshooting.md for optional imports, device selection, stale normalization, data layout, and evaluation drift.

Long training, external model/data acquisition, trackers, and policy servers are never default smoke tests. State the exact task, simulator, device, dataset, checkpoint, and backend actually exercised.

Task implementation routes to task-development; integration data/replay routes to benchmark-integrations.