Don't be so Stief! Learning KV Cache low-rank approximation over the Stiefel manifold

arXiv:2601.21686v2 Announce Type: replace Abstract: Key-value (KV) caching enables fast autoregressive decoding but at long contexts becomes a dominant bottleneck in High Bandwidth Memory (HBM) capacity and bandwidth. A common mitigation is to compress cached keys and values by projecting per-head matrices to a lower rank, storing only the projections in the HBM. However, existing post-training approaches typically fit these projections using SVD-style proxy objectives, which may poorly reflect end-to-end reconstruction after softmax, value mixing, and subsequent decoder-layer transformations.

Source: arXiv cs.LG — read the full report at the original publisher.

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