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プレフィックスのキーと値は後から重要になる:ループ型言語モデルにおける自己推測的デコードのためのRIPEキャッシュ

arXiv:2610.34436v1 発表タイプ:新規 要旨:ループ型言語モデルは、各トークンに対して層のブロックを$R$回実行するため、そのキーバリューキャッシュ(KVキャッシュ)には各再帰層ごとに$R$個の平面が格納され、自己予測的デコーディング(一部のループで予備的に推論し、残りの$R$すべてで検証する手法)ではそれらすべてを読み込む。我々は、プレフィックスのキーとバリューが影響を及ぼすのは後半に集中していることを確認した。同程度の摂動サイズにおいて、ループ25〜32はループ2〜9に比べて、Huginn-0125では$e^{15.0}$倍、Raven-Llamaでは$e^{12.8}$倍も感度が高く、各平面はそれぞれのループごとに0.88および0.81の割合で最後の平面へと幾何級数的に近づいていく。このとき、最も深い平面単独で十分かどうかは、後半の平面がどれほど成熟しているかに依存する。Ravenの場合、その平面のみを保存してもbf16からfp32への変換と区別できない一方、Huginnでは位置のわずか1%が全体の誤差の74%を占めている。これをRIPEに展開すると、最も深い

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Abstract:A looped language model runs one block of layers $R$ times per token, so its key-value (KV) cache holds $R$ planes per recurrent layer, and self-speculative decoding (draft with a few loops, verify with all $R$) reads them all. We find that prefix keys and values matter late: at equal perturbation size, loops 25-32 are $e^{15.0}$ (Huginn-0125) and $e^{12.8}$ (Raven-Llama) times more sensitive than loops 2-9, and the planes approach the last one geometrically, at 0.88 and 0.81 per loop. Whether the deepest plane alone suffices then depends on how ripe the late planes are: on Raven, storing only it is indistinguishable from a bf16-to-fp32 change; on Huginn, 1% of positions carry 74% of its error. We turn this into RIPE, which stores the deepest plane everywhere except a window of recent positions kept at full depth. On Huginn, a 64-position window removes 89% of the tail positions at 11.9% of the full cache's memory (1024-token context), and any window at least as long as the draft block makes batched verification block-independent by construction. Nine pre-registered GPU batches back these results. Simple models of acceptance, decoding cost and memory, two of them checked on held-out measurements, account for the measured self-speculative speed-ups ($2.09\times$ with the full cache, $2.58\times$ with the deepest plane) and decompose the $2.24\times$ fixed-memory throughput gain of one shared plane.

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