Data Curation and Synthetic Data › Summary
Michael BrenndoerferJuly 4, 20262 min read
World Models HandbookAll chapters Part 6 of 12Learning World Models at Scale
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- A world model is a compression of its training distribution, so curation determines what the model can learn. It is a modeling decision, not a preprocessing chore.
- Quality is not one number: it decomposes into signal-to-noise, temporal alignment, action fidelity, episode completeness, and label correctness, each with its own detector. It is also model- and task-relative, so define the target before writing filters.
- Coverage and density are distinct diagnostics. Nearest-neighbor distances and density estimates in a task-relevant embedding can flag holes or sparse regions, but they inherit the representation and are not proofs of model error.
- Deduplication can operate at episode and transition granularity. Exact hashing catches exact copies; shingled or embedding-based similarity rules flag candidates for calibrated downweighting or removal.
- Filtering suspected corruption changes the training distribution. Relative thresholds adapt to scale, absolute thresholds encode external bounds, and both require per-regime validation.
- Balancing reshapes the training objective toward a stated target distribution. Reweighting, resampling, and group-robust objectives have different bias, variance, and repetition properties; resampling repeats examples from scarce groups.
- Synthetic data trades empirical fidelity for controllable coverage. Simulation, procedural generation, and learned rollouts inherit their specifications and must be validated against real held-out data.
- Recursive training can lose tails or modes when generated data replaces or overwhelms real data. Frozen real anchors, bounded rollout horizons, generator diversity, and per-regime monitoring are useful safeguards, not guarantees.
- Mixture weights change the population risk optimized within a fixed model class. Choose them with validated proxy sweeps, direct response-surface fitting, or explicit balancing heuristics, and write down the target distribution.
- Provenance records source, collection context, processing history, and license for every episode. It is the prerequisite for reproducibility, for debugging, and for compliance.
- Split by the factor you want to generalize across, such as trajectory, scene, session, or embodiment, and verify identifiers, exact reuse, and suspicious near-duplicates mechanically. Hold out evaluation data before training any generator used for augmentation.
- Match evaluation to the downstream decision. The worked example measures autoregressive state rollout under fixed recorded actions; a planner-facing claim additionally requires running the planner and reporting task outcomes per regime.