Beyond Validation Loss: Coverage and Reliability Dynamics in Robot Policy Pretraining
Overview
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.
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“at explicitly account for posttraining. Blog post, visualizations, and code are available at https://zhanyisun.github.io/robot-policy-learning-dynamics/.”
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- arXiv Robotics — research abstractsSignalBeyond 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.
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