# Long-term
- Implement a dataloader in-house, with a clear, lightweight mechanism for
  collection-of-structures to structure-of-collections. For multi-proc handling
  (happens in torch's dataloader, as well as the BatchedDataset for two
  different purposes), we should rely on (a hopefully more stable) `execlib`.
- `Domains` may be externalized (`co3` or `convlib`)
- Up next: CLI, fully JSON-ification of model selection + train.
- Consider a "multi-train" alternative (or arg support in `train()`) for
  training many "rollouts" from the same base estimator (basically forks under
  different seeds). For architecture benchmarking above all, seeing average
  training behavior. Consider corresponding `Plotter` methods (error bars)