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CANDO: Conservative Action-Noise Directional Optimization for Residual Reinforcement Learning

This paper introduces CANDO, a residual reinforcement learning method that improves frozen generative policies by combining noise-space and action-space residual optimization. CANDO removes fixed latent feasible regions and uses conservative value estimation with gated execution to regulate adaptation. Experiments show CANDO achieves 76.1 NAUC and 91.4% final success rate, surpassing single-space baselines by 6.6 NAUC and 4.1%.

arxiv1d agoScore 78

Reinforcement LearningRobot ControlPolicy Adaptation

Research Editor selected

AegisFlow Proposes Training-Free Guidance for Safer Generative Trajectories

AegisFlow is a training-free framework designed to guide pretrained generative models toward target outcomes while maintaining constraints throughout generation. It formulates guidance as a global optimal-control problem, incorporating a time-varying barrier function and a tractable approximation of the Hamilton–Jacobi–Bellman equation. The authors report improved trade-offs between target alignment and constraint satisfaction in text-guided image manipulation and robotic planning, and provide a code repository.

Why it matters: The research offers a concrete approach to constraint-aware generation, with reported experiments spanning image manipulation and robotic planning and code available for evaluation.

arxiv6d agoScore 82

AegisFlowGenerative modelsConstraint-aware generation