Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
Overview
This research paper presents Large Discrete Policy (LDiP), a fully discrete behavior modeling framework that selects actions from a large vocabulary of physically plausible candidates. Unlike continuous generative models that rely on iterative denoising, LDiP employs stochastic iterative scoring to progressively re-score and prune candidates, enabling fine-grained ranking and exploration while maintaining an explicit decision process. The authors demonstrate that LDiP outperforms strong discrete and continuous baselines in autonomous driving and matches or exceeds continuous generative policies in robotic manipulation across end-to-end planning, closed-loop driving, and vision-language-action settings.
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- arXiv Robotics — research abstractsSignalLarge Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
This research paper presents Large Discrete Policy (LDiP), a fully discrete behavior modeling framework that selects actions from a large vocabulary of physically plausible candidates. Unlike continuous generative models that rely on iterative denoising, LDiP employs stochastic iterative scoring to progressively re-score and prune candidates, enabling fine-grained ranking and exploration while maintaining an explicit decision process. The authors demonstrate that LDiP outperforms strong discrete and continuous baselines in autonomous driving and matches or exceeds continuous generative policies in robotic manipulation across end-to-end planning, closed-loop driving, and vision-language-action settings.
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