用可检索的梯度实现持续训练，避免参数漂移。
Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift
- 将梯度存为可检索的知识单元，按需调用临时更新模型。
- 在通用与领域任务上优于对比方法，保持性能不下降。
- 适合需要持续学习且避免遗忘的部署场景。
持续后训练使模型在部署后持续吸收新知识，但反复更新共享参数会导致权重漂移，引发灾难性遗忘并降低泛化能力。检索增强生成虽能避免参数漂移，但缺乏参数化知识整合的深度。本文提出 ReGrad（可检索梯度），将梯度视为可检索的知识单位：离线预计算文档特定梯度，存入索引梯度库，在推理时仅检索相关梯度进行临时权重调整。然而，原始语言模型梯度针对的是词元级文档重建，而非查询驱动的知识利用。为此，我们引入双层元学习目标，将文档衍生梯度重塑为适用于下游任务的可迁移适应信号。跨通用与领域设置的实验表明，ReGrad 在性能上超越 CPT 与 RAG 基线，实现可扩展、可逆的参数化知识注入，且无累积权重漂移。
原文摘要 · Abstract (English)
Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate weight drift, potentially causing catastrophic forgetting and degrading general capabilities. Retrieval-augmented generation avoids such parameter drift, yet often lacks the depth of parametric knowledge integration. In this paper, we propose ReGrad (Retrievable Gradients), a new paradigm that treats gradients as retrievable units of knowledge. ReGrad pre-computes document-specific gradients offline, stores them in an indexed Gradient Bank, and retrieves only query-relevant gradients at inference time for temporary weight adaptation. However, raw language-modeling gradients are optimized for token-level document reconstruction rather than for query-driven knowledge use. We therefore introduce a bi-level meta-learning objective that reshapes document-derived gradients into generalizable adaptation signals for downstream tasks. Experiments across general and domain-specific settings show that \textsc{ReGrad} outperforms CPT and RAG baselines, enabling scalable and reversible parametric knowledge injection without accumulating weight drift.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。