SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models
Delta-Rule recurrent models maintain a fixed-size state, enabling $O(1)$ inference memory but potentially becoming unstable under extreme-context extrapolation. By tracking RWKV-7 over sequences of up to 100M tokens, we empirically identify a distinct failure pattern: \textbf{localized norm explosion atop a relatively sparse substrate}, rather than global state saturation. Analysis of the recurrent update suggests that persistent decay keeps weakly updated entries small, whereas uneven injections allow a few channels to accumulate extreme values. Motivated by this diagnosis, we propose \textbf{State Anomaly Neutralization (SANE)}, which applies adaptive $\tanh$ compression at chunk bounda...
| Authors | Qingwen Lin, Boyan Xu, Xiao Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 August 2026 |
| Last updated at source | 25 August 2026 |
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