The Failure Mode of Low-Precision Recurrent States

Temporal models that rely on recurrent states (such as RNNs, LSTMs, or state-space models) face a unique challenge when subjected to standard quantization techniques. While weight quantization is common and generally stable, quantizing the recurrent state—the "memory" of the model—leads to rapid error accumulation. Because these states are updated iteratively, even minor rounding errors introduced by low-precision representation (e.g., INT8 or FP8) compound over time. This results in "state drift," where the model's internal representation of history diverges from the high-precision baseline, eventually leading to complete output degradation in long-sequence tasks.

The Write-Back Mitigation Strategy

The authors demonstrate that simply forcing recurrent states into low-precision formats is insufficient for maintaining long-term coherence. They propose a 'Recurrent-State Write-Back' mechanism, which selectively maintains high-precision buffers for critical state components. By identifying which parts of the hidden state contribute most to temporal stability, the system performs a 'write-back' operation: it computes updates in high precision and only quantizes the state for storage or secondary operations. This hybrid approach balances the memory-bandwidth benefits of low-precision inference with the numerical stability required for long-context temporal modeling.

Practical Implications for Deployment

For engineers deploying temporal models on edge hardware or memory-constrained environments, this research suggests that aggressive quantization of the hidden state is a primary failure vector. The paper argues that developers should not treat recurrent states as standard activations. Instead, they should implement a tiered precision strategy where the recurrent loop remains in higher precision (e.g., FP16 or BF16) while the feed-forward layers are quantized to lower bit-widths. This approach prevents the catastrophic memory loss observed in fully quantized recurrent architectures without sacrificing the throughput gains typically sought through quantization.