Large Discrete Policy: Advancing Explicit Behavior Modeling with Stochastic Iterative Scoring
Behavior policies are often formulated as continuous generative models, whose iterative denoising processes are expressive but difficult to interpret and prone to producing implausible actions. We propose the Large Discrete Policy (LDiP), a fully discrete behavior modeling framework that selects actions from a large vocabulary of physically plausible candidates. Rather than perturbing actions, LDiP improves expressivity through stochastic iterative scoring: it progressively re-scores and prunes candidates with score-space stochasticity, enabling fine-grained ranking and exploration among plausible actions while preserving an explicit decision process. Across end-to-end planning, closed-lo...
| Authors | Zhenxin Li, Nadine Chang, Xinglong Sun et al. |
|---|---|
| Primary category | cs.RO |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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