Progressive context extension may suit linear-attention hybrids better than short-SWA models
stochasticchasm · x · 2026-07-28
Progressive context extension may matter more for linear-attention hybrids than short-SWA models
The post argues that progressive context extension is especially important for linear attention hybrids, because both the global and local layers need to adapt as sequence length grows. By contrast, in short-SWA hybrids, only the global layers need the same degree of adaptation.
A reply adds that synthetic data can be particularly useful for long-context training, especially because real long-context data is scarce. It also suggests the method may be closely related to the recent megadocs line of work.
The accompanying image reinforces the idea of gradually growing context during training as a way to make long-range dependencies tractable.
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