HUD Documentation - Evaluations and RL Environments.

You create the model on the platform, run the training loop from Python, then come back to the model page to see what changed.

Create a trainable model

Open Models, select New model, and choose a trainable base. HUD creates a private model for your team with its own API name and checkpoint history. The same operation is available from the CLI:

hud models fork Qwen/Qwen3.5-4B --name arith-rl

Train from Python

Run a taskset in groups and pass the graded rollouts to TrainingClient. Each trainer.step(...) applies the update, saves a checkpoint, and promotes it for the next rollout. The model API name stays the same. Training changes the weights behind it, not the environment or taskset.

Inspect the model

Return to the model page after a training step:

Tab	What to look for
Results	Evaluation jobs run with this model.
Checkpoints	The checkpoint tree and the weights currently used for inference.
Logs	Output from training and model operations.

To inspect checkpoints from the terminal:

hud models checkpoints arith-rl
hud models head arith-rl --set <checkpoint-id>

The training agents guide contains the complete Python loop. See the TrainingClient reference for losses and checkpoint operations.

Start here

Core workflows

Platform features

Reference

Training

Create a trainable model

Train from Python

Inspect the model

​Create a trainable model

​Train from Python

​Inspect the model

Create a trainable model

Train from Python

Inspect the model