Beyond Validation Loss: Coverage and Reliability Dynamics in Robot Policy Pretraining
Beyond Validation Loss: Coverage and Reliability Dynamics in Robot Policy Pretraining
This research paper introduces pass@k metrics to evaluate robot policy pretraining, demonstrating that large-k pass@k captures reliability dynamics and coverage-reliability tradeoffs not visible in standard validation loss or mean task success (pass@1). The authors propose offline checkpoint-selection criteria and generative data augmentation procedures to balance these factors. Results are available via the provided arXiv link and accompanying blog post with code.
The authors introduce pass@k metrics to evaluate robot policy pretraining, demonstrating that large-k pass@k captures reliability dynamics and coverage-reliability tradeoffs not visible in standard validation loss or mean task success (pass@1).
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- code: Reported pending Open source S6
- weights: not_reported
- dataset: not_reported
- paper: not_reported
- code: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
at explicitly account for posttraining. Blog post, visualizations, and code are available at https://zhanyisun.github.io/robot-policy-learning-dynamics/.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org