[
    {
        "id": "osp-21167",
        "type": "article-journal",
        "title": "Learn the Directions, Normalize the Gains: Post-Training Normalization for LoRA",
        "author": [
            {
                "family": "Tian",
                "given": "Zailong"
            },
            {
                "family": "Chen",
                "given": "Yanzhe"
            },
            {
                "family": "Han",
                "given": "Zhuoheng"
            },
            {
                "family": "Wang",
                "given": "Houfeng"
            },
            {
                "family": "Liao",
                "given": "Lizi"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/learn-the-directions-normalize-the-gains-post-training-normalization-for-lora",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "While Low-Rank Adaptation (LoRA) enables efficient task specialization, its learned updates can compromise capabilities beyond the target task. We identify \\textbf{adaptation imbalance}: a few singular directions dominate the trained update, leaving its performance sensitive to how gains are allocated. We argue that \\textbf{learning where to adapt does not ensure that adaptation gains are well balanced}. This motivates \\textbf{LoRA-Norm}, a post-training normalization method that retains learned directions while rebalancing their gains. LoRA-Norm combines spectral rebalancing, a fixed nonlinear transformation of singular values, with nuclear-norm restoration, which preserves the original total spectral mass. It requires no calibration data or additional training and introduces no inference overhead. Across two backbones and three adaptation tasks, LoRA-Norm improves average specialization and capability retention, outperforming the evaluated post-hoc spectral pruning and gradient-guided editing configurations on both measures. Stronger functional equalization brings no consistent additional gains, revealing that balancing adapter gains and equalizing their responses are distinct objectives."
    }
]