CTEM unifies density estimation via a bounded energy-difference transform that yields a sample-only objective with constant target 1, recovering log p without partition functions or unbounded ratio regression.
International Conference on Machine Learning , pages =
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ReflectDrive-2 combines masked discrete diffusion with RL-aligned self-editing to generate and refine driving trajectories, reaching 91.0 PDMS on NAVSIM camera-only and 94.8 in best-of-6.
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Constant-Target Energy Matching: A Unified Framework for Continuous and Discrete Density Estimation
CTEM unifies density estimation via a bounded energy-difference transform that yields a sample-only objective with constant target 1, recovering log p without partition functions or unbounded ratio regression.
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ReflectDrive-2: Reinforcement-Learning-Aligned Self-Editing for Discrete Diffusion Driving
ReflectDrive-2 combines masked discrete diffusion with RL-aligned self-editing to generate and refine driving trajectories, reaching 91.0 PDMS on NAVSIM camera-only and 94.8 in best-of-6.