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TrainRation

Decide what to train on, and never move the yardstick.

Data rationing and immutable evaluation yardsticks for robot learning.

Status: early, pre-alpha

This release reserves the package name for an in-development project. There is no working functionality yet. The version number (0.0.1) and the Development Status :: 1 - Planning classifier are accurate, not decoration — please do not depend on this package in anything real.

If you are looking for something usable today, this is not it. Watch github.com/trainration instead.

What it will do

Robot learning data is expensive to collect and much of it is redundant. TrainRation works on two problems:

  1. Deciding what to train on — dataset selection, mixture ratios, curriculum ordering, and failure attribution over LeRobot v3.0 datasets.
  2. Keeping the yardstick fixed — the evaluation criteria that decide whether a change helped must not be reachable by the process that proposes changes. A self-improving system that can edit its own success criteria will optimize the criteria instead of the policy.

The second point is the design constraint that shapes the first. Research direction evolves; the measuring stick does not.

Planned components

Package Scope
trainration-probe Cross-dataset quality and consistency checks for LeRobot v3.0
trainration-yardstick Evaluation criteria and admission constraints as structural, non-bypassable boundaries
trainration-core Recursive decision kernel: mixture ratios, failure attribution, curriculum

License

MIT

Metadata

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