SpectralShift: Effective Context Window Extension of Gated DeltaNet via Spectral Reparameterization
SpectralShift extends Gated DeltaNet context windows via spectral reparameterization of alpha projections, improving long-context capabilities without architectural changes.
The paper studies long-context extension of Gated DeltaNet (GDN) linear attention from the spectral perspective of its transition matrix, identifying two key factors: a broad slow spectral band aligned with target dependency lengths, and preserved fast-decaying modes for state clearing and context switching. SpectralShift reparameterizes the alpha projections initialization to reshape the decay spectrum and adds a learning-rate scaling for alpha projections during long-context continual pretraining. Experiments show consistent long-context improvements over direct continued pretraining, and the code is open-sourced.
- Identifies slow spectral band breadth and fast-decaying modes as governing long-range retrieval.
- Reparameterizes alpha projection initialization to enhance slow propagation capacity.
- Adds learning-rate scaling for alpha projections to facilitate long-context training.
- Code open-sourced at github.com/RUCAIBox/GDN-SpectralShift.
Full article171 words · extracted from huggingface.co · click to collapse
Recently, linear attention layers have been increasingly adopted to replace softmax attention at scale for long-context modeling. However, existing context extension approaches typically apply continued pretraining directly without modifying these layers, overlooking the spectral properties of linear attention state dynamics. In this work, we study long-context extension of Gated DeltaNet (GDN) from a spectral perspective of transition matrix and identify two essential factors governing long-range information retrieval: (1) a sufficiently broad slow spectral band aligned with the target dependency length, and (2) the preservation of fast-decaying modes for state clearing and context switching. Based on this observation, we propose SpectralShift, a spectral reparameterization approach for long-context continual pretraining of GDNs. Specifically, SpectralShift reparameterizes the alpha projections initialization to reshape the decay spectrum by enhancing slow propagation capacity, and further introduces a learning-rate scaling for alpha projections to facilitate long-context training. Experiments show that SpectralShift consistently improves long-context capabilities over training, providing an effective and efficient solution for extending context windows of linear attention models. The code has been open-sourced at https://github.com/RUCAIBox/GDN-SpectralShift.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2609.14320