Reinforcement learning

Reinforcement learning trains a policy from rewards instead of labels: the policy acts, a reward scores what it did, and an update moves the policy toward what scored well. The loop is the same everywhere, but where the time and the risk go is not. These tutorials cover very different shapes of it.

Durable agentic RL with SkyRL and Harbor Make SkyRL/Harbor-style agentic RL rollouts durable — trials that survive driver crashes, judges that are never re-billed, and training steps that apply exactly once.

Reinforcement learning in the user guide: environments, rewards, rollouts, the training loop, and evaluation.

Reinforcement learning

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