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We show that these contradictions arise from a single overlooked factor: the batch size. When properly tuned, vanilla LoRA often matches the performance of more complex variants. We further propose a proxy-based, cost-efficient strategy for batch size tuning, revealing the impact of rank, dataset size, and model capacity on the optimal batch size. Our findings elevate batch size from a minor implementation detail to a first-order design parameter, reconciling prior inconsistencies and enabling more reliable evaluations of LoRA variants.","title":"Beware of the Batch Size: Hyperparameter Bias in Evaluating LoRA","url":"https://arxiv.org/abs/2602.09492","vendor":"arxiv_cs_ai"},"summary":"arXiv:2602.09492v2 Announce Type: replace-cross \nAbstract: Low-rank adaptation (LoRA) is a standard approach for fine-tuning large language models, yet its many variants report conflicting empirical gains, often on the same benchmarks. 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