StructZO: Structured Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning
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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.