SAGE: Sign-Adaptive Gradient for Memory-Efficient LLM Optimization
The AdamW optimizer, while standard for LLM pretraining, is a critical memory bottleneck, consuming optimizer states equivalent to twice the model's size. Although light-state optimizers like SinkGD attempt to address this issue, we identify the embedding layer dilemma: these methods fail to handle the sparse, high-variance gradients inherent to embeddings, forcing a hybrid design that reverts to AdamW and partially negates the memory gains. We propose SAGE (Sign Adaptive GradiEnt), a novel optimizer that resolves this dilemma by replacing AdamW in this hybrid structure. SAGE combines a Lion-style update direction with a new, memory-efficient $O(d)$ adaptive scale. This scale acts as a "s...
| Authors | Wooin Lee, Hyun-Tae Kim |
|---|---|
| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 15 April 2026 |
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