Quantization Breaks RNN Memory: Error Feedback Restores GRU/LSTM Accuracy Without Retraining
Ismail Erbas · hf · 2026-09-07
This work identifies a concrete failure mode in quantized recurrent inference: state write-back rules suppress small updates, degrading temporal memory of GRUs and LSTMs at low precision.
The authors propose error feedback and residual memory mechanisms that compensate for the suppressed updates, restoring accuracy without any retraining across GRU and LSTM architectures—a practical reference for on-device recurrent deployment.
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