Rethinking Diffusion for Neural Channel Decoding: Aligned Training and Heterogeneous Inference

Neural channel decoders offer a common approach to decoding different families of linear block codes, but improving error correction often comes at the cost of slower inference. Diffusion decoders make this trade-off particularly apparent: they rely on repeated model evaluations, while existing training objectives do not always match the dynamics used at inference. We revisit diffusion decoding by aligning the training target with probability-flow dynamics and mapping the channel noise level directly to diffusion time. This yields DEER, a decoder that supports a fixed inference schedule and improves decoding performance in both one-step and multi-step settings. To reduce the cost of each step, we develop FAWN, a lightweight decoder with computation that scales linearly with the input sequence length and the number of nonzero entries in the parity-check matrix. We further observe that the high-capacity decoder is most useful in the noisier stages of a decoding trajectory, whereas lightweight decoders become competitive as the noise decreases. This motivates heterogeneous inference, which assigns DEER and FAWN to different stages of the same trajectory. Across several code families, the resulting decoders improve the measured bit-error-rate–latency trade-off, with heterogeneous inference matching or exceeding the performance of the high-capacity decoder at lower latency.