SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models
Researchers have proposed State Anomaly Neutralization (SANE), a method to stabilize extreme-context delta-rule models. These models can become unstable when dealing with long sequences of tokens. SANE applies adaptive compression at chunk boundaries to prevent localized norm explosion, which was identified as the primary cause of instability in RWKV-7 models. In experiments, SANE matched the baseline performance on 11 reasoning benchmarks and retained functional reasoning af
Researchers have proposed State Anomaly Neutralization (SANE), a method to stabilize extreme-context delta-rule models. These models can become unstable when dealing with long sequences of tokens. SANE applies adaptive compression at chunk boundaries to prevent localized norm explosion, which was identified as the primary cause of instability in RWKV-7 models. In experiments, SANE matched the baseline performance on 11 reasoning benchmarks and retained functional reasoning after a 100M-token prefix, while the baseline encountered numerical overflow.
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Why it matters: This matters because it addresses a fundamental challenge in developing stable and scalable deep learning models for long-range dependencies, which is crucial for many applications such as natural language processing and computer vision.
Source: https://arxiv.org/abs/2608.22354
This article was originally published at: https://arxiv.org/abs/2608.22354