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

arXiv:2608.22322 · cs.LG · Submitted 2026-08-23 · Read on arXiv

cs.LG

Submitted: 2026-08-23

Updated: 2026-09-01

Terminology

• Quantization — 44× in this paper · explained in 12 episodes
• Trace — 16× in this paper · explained in 2 episodes
• Adam — 14× in this paper · explained in 2 episodes
• Adaptive Log-Space — 9× in this paper
• Benchmark — 8× in this paper · explained in 2 episodes
• Perplexity — 7× in this paper · explained in 3 episodes
• Core — 5× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Reconstruction Error — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Ablation — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Allocator — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training
• Understanding Quantization of Optimizer States in LLM Pre-training: Dynamics of State Staleness and Effectiveness of State Resets
• COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training
• Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics
• TinyLlama: An Open-Source Small Language Model

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