arXiv:2605.28760v1 Announce Type: new Abstract: Zeroth-order (ZO) fine-tuning is attractive for large language models because it replaces backpropagation with forward objective evaluations. Existing implementations nevertheless execute ZO algorithms inside conventional training loops, even though their dominant work is repeated scoring under nearby parameter states. This creates a workload-runtime mismatch: the algorithm asks for structured inference-style scoring, while the system exposes a sequence of fragmented training-loop steps. We show that LLM ZO fine-tuning is an inference-dominated w
This research addresses a fundamental efficiency issue in LLM fine-tuning, driven by the increasing computational demands of large models and the need for more agile development methods.
Improving the efficiency of LLM fine-tuning can significantly reduce compute costs and accelerate AI development cycles, making advanced AI capabilities more accessible and adaptable.
By reframing zeroth-order fine-tuning as an inference workload, developers can leverage existing inference-optimized hardware and software, leading to substantial performance gains.
- · AI compute infrastructure providers
- · LLM developers
- · Cloud providers
- · AI model deployers
- · Inefficient AI training frameworks
- · Current backpropagation-heavy methods
Faster and cheaper model adaptation for various applications and data changes.
Reduced barriers to entry for deploying and customizing advanced LLMs, fostering wider innovation.
Increased competition among foundation model providers as custom fine-tuning becomes more democratized and efficient.
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Read at arXiv cs.LG