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Online critic backpropagation for flow-based robot policies is completely unnecessary: amortizing trajectory-aware adjoint costates into a lightweight network achieves superior value alignment at 3.6× the speed.
GRAS achieves superior training-free reward alignment in discrete diffusion models, outperforming previous methods and rivaling fine-tuned approaches with no added computational cost.