Train on the Syndrome Stream You Will Decode

Quantum error correction (QEC) turns repeated syndrome measurements into correction decisions. Neural decoders have gained widespread attention because they can learn complex, hardware-specific noise correlations directly from data. Neural decoders are usually trained and judged on reset-bounded memory experiments, assuming that benchmark winners remain best during uninterrupted computation. However, we find that this ranking can reverse. On never-reset surface-code streams at $d=5,7,9$, convolutional checkpoints that beat correlated matching by 12-17% on memory benchmarks fall behind it by $1.5\text{-}1.7\times$ on interior decisions, while released graph and transformer decoders show the same failure. To identify the source of this reversal, we use controlled interventions, which trace it to preparation and terminal-readout boundaries rather than sequence length. Building on this diagnosis, we fine-tune on interior-window labels. This restores the neural lead across all three families and, with one-window lookahead, reduces convolutional per-commit error by 40-45% versus matching in simulation. The same lesson holds for lattice surgery: a better frame predictor need not yield a better conditional parity, whereas joint frame–readout supervision improves the combined decision, and the jointly trained system lowers conditional parity error by 9-15% versus joint matching. Together, these findings make existing neural decoders stream-ready with a single fine-tuning stage and pave the way for neural decoding in fault-tolerant quantum computation.