Stratified Sparse Attention (SSA) reduces long-context inference costs by combining deterministic retrieval of high-scoring tokens with score-guided sampling of the residual. This approach minimizes the gap between sparse and dense attention at extreme sparsity levels.
HOW THIS AFFECTS YOU
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builderThis method offers a way to handle longer contexts with lower KV cache memory overhead.
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researcherYou can implement SSA to improve sparse attention efficiency without the high variance of pure sampling.