Differentiable planning promises end-to-end differentiability and adaptivity.However, an issue prevents it from scaling up to larger-scale problems: theyneed to differentiate through forward iteration layers to compute gradients, which couples forward computation and backpropagation and needs to balance forward planner performance and computational cost of the backward pass.To alleviate this issue, we propose to differentiate through the Bellman fixed-point equation to decouple forward and backw…