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arXiv Machine Learning By Yan Wang

Beyond Dense Adam States: Adaptive Log-Space Quantization for Memory-Efficient Optimizers

Read the original on arXiv Machine Learning →

The paper introduces Adaptive Log‑Space (AL) quantization, a block‑wise representation that adapts the non‑zero range per block and preserves the exact‑zero invariant for non‑negative optimizer states. AL8 and AL16 are combined with signed‑momentum encodings and state‑specific precision choices, rather than a single policy for all states. Experiments on TinyLlama‑1.1B and GPT‑2 show that AL‑based quantization can match or exceed full‑precision performance while dramatically reducing optimizer‑state storage.

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