SignSGD provably beats SGD by a factor of d under sparse noise via matched ℓ1-norm upper and lower bounds, with an equivalent result for Muon on matrices, and this predicts faster GPT-2 pretraining.
Convergence rate analysis of lion
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ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
LMO-IGT achieves O(ε^{-3.5}) iteration complexity for stochastic LMO optimization via implicit gradient transport with a single gradient per step and introduces the regularized support function as a unified stationarity measure.
StoSignSGD resolves SignSGD divergence on non-smooth objectives via structural stochasticity, matching optimal convex rates and improving non-convex bounds while delivering 1.44-2.14x speedups in FP8 LLM pretraining.
CLion achieves O(1/N) generalization error and O(√d / T^{1/4}) convergence for nonconvex stochastic optimization, improving on Lion's O(1/(N τ^T)) bound.
MLorc compresses optimizer momentum with low-rank methods to enable memory-efficient full fine-tuning of LLMs, outperforming LoRA and GaLore while matching full-parameter performance at small ranks.
citing papers explorer
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When and Why SignSGD Outperforms SGD: A Theoretical Study Based on $\ell_1$-norm Lower Bounds
SignSGD provably beats SGD by a factor of d under sparse noise via matched ℓ1-norm upper and lower bounds, with an equivalent result for Muon on matrices, and this predicts faster GPT-2 pretraining.
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
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Accelerating LMO-Based Optimization via Implicit Gradient Transport
LMO-IGT achieves O(ε^{-3.5}) iteration complexity for stochastic LMO optimization via implicit gradient transport with a single gradient per step and introduces the regularized support function as a unified stationarity measure.
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StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models
StoSignSGD resolves SignSGD divergence on non-smooth objectives via structural stochasticity, matching optimal convex rates and improving non-convex bounds while delivering 1.44-2.14x speedups in FP8 LLM pretraining.
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CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization
CLion achieves O(1/N) generalization error and O(√d / T^{1/4}) convergence for nonconvex stochastic optimization, improving on Lion's O(1/(N τ^T)) bound.
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MLorc: Momentum Low-rank Compression for Memory Efficient Large Language Model Adaptation
MLorc compresses optimizer momentum with low-rank methods to enable memory-efficient full fine-tuning of LLMs, outperforming LoRA and GaLore while matching full-parameter performance at small ranks.