Auditing Demonstration Curation Metrics: Action-Only Scorers Fail on the Structural Defects That Degrade Imitation Policies
arXiv:2606.05588v1 Announce Type: cross Abstract: Imitation-learning policies inherit the quality of the demonstrations they are trained on, and a growing set of curation metrics promise to score and filter low-quality demonstrations automatically. These metrics are each validated on different data with different protocols, so it is unclear which of them actually identify the demonstrations that harm a policy. We build a controlled testbed in which demonstration defects are injected with known type, and audit seven curation metrics along two axes: how well each separates defective from clean d
The proliferation of AI systems across various applications, particularly in robotics and autonomous agents, highlights the immediate need for robust and reliable training data.
Improving the quality of demonstration data is critical for scaling AI, especially for tasks requiring high precision and safety, impacting commercial viability and adoption.
This research provides a more rigorous framework for evaluating and selecting training data, potentially leading to more reliable AI models and accelerating deployment across industries.
- · AI developers
- · Robotics companies
- · Autonomous systems integrators
- · AI quality assurance services
- · Developers relying on unvalidated data curation methods
- · Companies with low-quality demonstration datasets
More effective and efficient development of imitation learning policies through improved data curation.
Accelerated deployment and commercialization of AI agents and humanoid robotics due to enhanced reliability and safety.
Reduced costs and increased accessibility of advanced AI capabilities as development cycles shorten and model performance improves.
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