arXiv:2607. 01125v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables fine-tuning large language models when backpropagation is unavailable or memory-prohibitive, but existing methods often perturb full model weights or randomly constructed low-dimensional subspaces, yielding high-variance estimates and limited performance.
arXiv:2601. 04710v2 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) achieves strong performance but is often limited by the memory overhead of backpropagation.
arXiv:2606. 14970v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specific data.
Bilevel-ZOFO is a bilevel optimization framework that combines fast, low‑memory first‑order (FO) parameter‑efficient fine‑tuning (PEFT) in the inner loop with stable, memory‑efficient zeroth‑order (ZO) updates of the full model in the outer loop. This design reduces the variance of ZO gradient estimates, stabilizes training, and mitigates prompt sensitivity while preserving the full model’s capacity. Empirical results show Bilevel-ZOFO achieves 2–4× faster training than existing ZO and FO‑PEFT methods while maintaining similar memory efficiency, and it functions as an efficient meta‑learning algorithm for rapid few‑shot adaptation.
arXiv:2606. 05516v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables memory-efficient fine-tuning of large language models (LLMs) using only forward passes, but it remains unclear how useful adaptation is distributed across layers.
arXiv:2606. 02857v1 Announce Type: cross Abstract: Zeroth-order (ZO) optimization is a memory-efficient alternative to backpropagation for fine-tuning large language models, but its deployment is limited by the high variance of gradient estimation.
arXiv:2506. 21833v2 Announce Type: replace Abstract: Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization are increasingly proposed as memory-efficient, backpropagation-free alternatives for large language model (LLM) fine-tuning, yet their benefits are typically evaluated only against standard backpropagation (BP), omitting memory-efficient variants such as activation checkpointing.
arXiv:2608. 15665v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables backpropagation-free fine-tuning of large language models, but existing ZO methods suffer from high-variance gradient estimators, making convergence unstable and highly sensitive to learning rates.
The paper introduces TACO, a new optimizer for fine‑tuning large language models that drastically reduces optimizer state memory while preserving first‑order gradients. TACO selects the sign of the largest magnitude entry in each column of weight matrices, achieving a 174× reduction in persistent optimizer memory compared to AdamW8bit and a 2.9× decrease in peak training memory on OPT‑13B. This allows full‑parameter fine‑tuning of 30–32B‑parameter models on a single 80 GB GPU across multiple model families and tasks, with comparable accuracy and runtime to existing methods.
arXiv:2605.07111v3 Announce Type: replace-cross Abstract: Recent literature on fine-tuning Large Language Models highlights a fundamental debate. While Full Fine-Tuning (FFT) provides greater represe...
Sample-Adaptive Zeroth-Order Optimization (SAZO) is a memory‑efficient fine‑tuning method for large language models that learns sample importance online and allocates queries adaptively. It estimates importance using gradient‑norm information from directional differences and tracks evolving importance with per‑example exponential moving averages, requiring only O(N) additional scalar state. SAZO provably matches the leading convergence term of a minimum‑variance sampling oracle and achieves faster convergence than standard methods, with experiments showing up to 6× faster convergence to the same training loss.
The paper introduces LARS (Low-memory Activation-Rank Subspace), a parameter-efficient fine-tuning method that reduces the memory footprint of adapter activations by operating in a sequence-level low-rank subspace. By replacing token-level activations with pooled feature extraction, low-rank modulations, and residual projection, LARS cuts the stored activation intermediates from O(BSRL) to O(BRL). Experiments show that LARS achieves comparable performance to LoRA while lowering GPU memory usage by 23%, CPU memory usage by 47.8%, and reducing CPU power and energy consumption per optimization step by about 22%.