BoostLoRA: Growing Effective Rank by Boosting Adapters

• URL: http://arxiv.org/abs/2604.27308v1
• Date: Thu, 30 Apr 2026 01:43:00 GMT
• Title: BoostLoRA: Growing Effective Rank by Boosting Adapters
• Abstract summary: BoostLoRA is a gradient-boosting framework that overcomes the limit by iteratively training and merging minimal adapters.<n>It is the first PEFT method whose effective rank grows with training, separating per-round parameter cost from total representational capacity.
• Score: 3.022272657109967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Parameter-efficient fine-tuning (PEFT) methods face a tradeoff between adapter size and expressivity: ultra-low-parameter adapters are confined to fixed low-rank subspaces, capping performance even with extended training. We propose BoostLoRA, a gradient-boosting framework that overcomes this limit by iteratively training and merging minimal adapters on the examples the current model gets wrong. A ROTATE SVD basis strategy assigns each round to an orthogonal subspace, so cumulative effective rank grows linearly with the number of rounds while each adapter remains ultra-low-rank. After merging, adapters are discarded, leaving zero inference overhead. On Qwen2.5-3B, BoostLoRA reaches 89.1% on GSM8K and 68.8% on MATH-500, surpassing both the best single-shot ultra-low parameter adapter (TinyLoRA) and full fine-tuning; on code generation it reaches 57.2% on MBPP and 80.4% on HumanEval while full fine-tuning drops below the zero-shot baseline. We also demonstrate cross-architecture transfer on protein binding classification with ESM2-650M and cross-entropy training. BoostLoRA is, to our knowledge, the first PEFT method whose effective rank grows with training, separating per-round parameter cost from total representational capacity.

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