Papers with zeroth-order (ZO) optimization
RoZO: Geometry-Aware Zeroth-Order Fine-Tuning on Low-Rank Adapters for Black-Box Large Language Models (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated exceptional performance across a wide range of tasks, yet fine-tuning them efficiently under black-box or memory-constrained settings remains challenging. |
| Approach: | They propose a Riemannian zeroth-order optimization framework that constrains updates to the tangent space of the LoRA manifold. |
| Outcome: | The proposed framework achieves more stable convergence, tighter variance bounds, and superior performance compared to existing ZO methods. |
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers . fine-timing on resource-constrained edge devices presents significant memory and computational demands . |
| Approach: | They propose a resource-efficient fine-tuning framework for LLMs specifically designed for edge devices. |
| Outcome: | Experiments show that MobiZO achieves substantial runtime speedups and memory savings while improving fine-tuning accuracy. |
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) face memory challenges due to the high cost of backpropagation. |
| Approach: | They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B. |
High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing (2026.findings-acl)
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| Challenge: | Zeroth-order (ZO) optimization is a memory-efficient alternative to fine-tuning large language models (LLMs). |
| Approach: | They propose a zeroth-order (ZO) optimization framework that offloads model parameters to CPU memory and overlapping transformer block transfer with dual forward computation on a single GPU. |
| Outcome: | The proposed framework achieves a 3x speedup over ZO2 on an OPT-175B model while maintaining memory efficiency and improving training throughput. |