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While backpropagation (BP) is the mainstream approach for gradientcomputation in neural network training, its heavy reliance on the chain rule ofdifferentiation constrains the designing flexibility of network architectureand training pipelines. We avoid the recursive computation in BP and develop aunified likelihood ratio (ULR) method for gradient estimation with just oneforward propagation. Not only can ULR be extended to train a wide variety ofneural network architectures, but the computation flow in BP can also berearranged by ULR for better device adaptation. Moreover, we propose severalvariance reduction techniques to further accelerate the training process. Ourexperiments offer numerical results across diverse aspects, including variousneural network training scenarios, computation flow rearrangement, andfine-tuning of pre-trained models. All findings demonstrate that ULReffectively enhances the flexibility of neural network training by permittinglocalized module training without compromising the global objective andsignificantly boosts the network robustness.

One Forward is Enough for Neural Network Training via Likelihood Ratio Method