WORLD MODEL · ADVANCED TRAINING & EVALUATION

Training & Evaluation Lab

Explore advanced methods for multi-step training, latent dynamics, rollout stability, action fidelity, uncertainty, calibration, generalization and planning utility.

Train→ Roll Out→ Measure Drift→ Calibrate→ Plan→ Validate

Method Explorer

—Visible methods

—Training methods

—Evaluation methods

—Method categories

When to use it

Signals to track

Do not evaluate world models with a single number if the downstream system depends on long-horizon prediction, control or planning. Pair predictive metrics with task-level metrics.

Evaluation Stack

1 · One-step accuracyDoes the model predict the next state correctly?

2 · Multi-step rolloutHow quickly does error accumulate with horizon?

3 · Action fidelityDo different actions create the correct future differences?

4 · Long-horizon stabilityDoes the imagined world remain coherent?

5 · UncertaintyDoes confidence track prediction reliability?

6 · GeneralizationDoes the model survive distribution shift?

7 · Planning utilityDoes the world model improve decisions?

8 · EfficiencyWhat are the latency and compute costs of rollouts?

Included code examples

The repository includes compact PyTorch examples for multistep_loss.py, latent_overshooting.py, rollout_evaluation.py and uncertainty_ensemble.py.