超越全模型回滚：用于适配器状态多任务监督微调的AuroSFT
Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning
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中文总结 AI 辅助
针对多任务SFT的全模型回滚成本高的问题，提出参数高效框架AuroSFT，将状态转为可合并的适配器状态，在保留骨干网络的比较中平均准确率达61.36%，优于msft的59.85%。
中文摘要 AI 辅助
多任务监督微调（SFT）通常将异构数据混合视为单一优化问题，尽管不同任务可能在不同时刻达到最佳泛化性能。msft通过按任务的展开、排除和回滚暴露了这种不匹配，但它的原始公式将调度器状态体现为全模型检查点，使得阶段转换的存储、恢复和部署成本高昂。本文提出AuroSFT，这是一种参数高效的框架，将过拟合感知多任务SFT的承载状态重构为紧凑、可合并的适配器状态。AuroSFT冻结预训练骨干网络，仅训练注入的适配器，在按任务的峰值处回滚适配器检查点，并继续处理剩余的活跃混合数据。在层级别，每个适配器对低秩权重因子应用受AuroRA启发的自适应非线性层，而非对样本表示应用。由此产生的更新在输入中保持线性、秩受限，并且可精确合并到冻结的投影中。在保留骨干网络的比较协议下，AuroSFT实现了61.36%的平均准确率，而对应的msft参考行的平均准确率为59.85%，且在所有五个骨干网络上均获得更高的准确率。我们的代码可在匿名仓库获取：this https URL。
英文摘要
Multi-task supervised fine-tuning (SFT) often casts a heterogeneous data mixture as a single optimization problem, even though different tasks may reach their best generalization at different times. msft exposes this mismatch through task-wise roll-out, exclusion, and rollback, but its original formulation materializes the scheduler state as full-model checkpoints, making stage transitions costly to store, restore, and deploy. This paper introduces AuroSFT, a parameter-efficient framework that recasts the carried state of overfitting-aware multi-task SFT as a compact, mergeable adapter state. AuroSFT freezes the pretrained backbone, trains only injected adapters, rolls back adapter checkpoints at task-wise peaks, and continues on the remaining active mixture. At the layer level, each adapter applies an AuroRA-inspired adaptive nonlinear layer to a low-rank weight factor rather than to the sample representation. The resulting update remains linear in the input, rank-bounded, and exactly mergeable into the frozen projection. Under the retained-backbone comparison protocol, AuroSFT achieves 61.36% average accuracy, compared with 59.85% for the corresponding msft reference row, and obtains higher accuracy on all five backbones. Our code is available at the anonymous repository: https://anonymous.4open.science/r/AuroSFT-80D1.
