Adaptive correction scheduling for hard constraints in generative sampling recovers 71% of stepwise projection benefits using 75% fewer corrections by focusing on trajectory-perturbing steps.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A first-order Sobolev loss for diffusion policies enables warm-starting trajectory optimization solvers with 2×–20× speedup and fewer diffusion steps, using very few training trajectories.
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Enforcing Constraints in Generative Sampling via Adaptive Correction Scheduling
Adaptive correction scheduling for hard constraints in generative sampling recovers 71% of stepwise projection benefits using 75% fewer corrections by focusing on trajectory-perturbing steps.
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Accelerating trajectory optimization with Sobolev-trained diffusion policies
A first-order Sobolev loss for diffusion policies enables warm-starting trajectory optimization solvers with 2×–20× speedup and fewer diffusion steps, using very few training trajectories.