Why 4-Bit Memory Breaks Recurrent Models
π‘A 4-bit state can freeze recurrent memoryβsee the fixes that recover accuracy without retraining.
β‘ 30-Second TL;DR
What Changed
Deterministic 4-bit recurrent-state write-back increased estimation errors by approximately 70x for Ο1 and 300x for Ο2.
Why It Matters
The findings warn that quantizing recurrent activations is not merely a numerical-compression problem: the write-back rule changes the model's temporal dynamics. Practitioners deploying GRU or LSTM models on constrained hardware should evaluate state-update behavior separately from weight and activation precision.
What To Do Next
Benchmark your GRU or LSTM with quantized recurrent-state write-back, then test error feedback before lowering state precision further.
Key Points
- β’Deterministic 4-bit recurrent-state write-back increased estimation errors by approximately 70x for Ο1 and 300x for Ο2.
- β’Repeated updates below the write threshold are suppressed, causing the stored state to remain nearly unchanged despite ongoing model updates.
- β’Error feedback, residual memory, and direction memory restored accuracy without retraining the GRU.
- β’An independently trained LSTM showed similar coarse-write-back failures, with the cell state more sensitive than the hidden state.
- β’Precision sweeps showed that higher state precision can still hurt a fixed recurrent model unless training is matched to the state interface.
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Original source: ArXiv AI β
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