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| title: README | |
| emoji: 📏 | |
| colorFrom: gray | |
| colorTo: blue | |
| sdk: static | |
| pinned: false | |
| # TrainRation | |
| > *Decide what to train on, and never move the yardstick.* | |
| Data rationing and immutable evaluation yardsticks for robot learning. | |
| ## Status: early, pre-alpha | |
| Nothing here yet. This organization is reserved for an in-development project; | |
| datasets and models will appear as the work becomes reproducible rather than | |
| merely runnable. | |
| Current artifacts live elsewhere: | |
| - **Source** — [github.com/trainration/trainration](https://github.com/trainration/trainration) | |
| - **PyPI** — [`trainration`](https://pypi.org/project/trainration/) (`0.0.1`, name reservation, no functional API) | |
| ## 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](https://github.com/huggingface/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 | | |
| ## What will land here | |
| This org is the home for the data-side artifacts: | |
| - Curated LeRobot v3.0 dataset subsets, with the selection criteria published | |
| alongside them | |
| - Held-out evaluation splits that are versioned and immutable once released | |
| - Probe reports over public robot learning datasets | |
| License: MIT | |