用进化策略优化数据筛选，提升AI智能体后训练效率的新范式。
arXiv:2607.06140v1 Announce Type: new Abstract: Large language model (LLM) agents require post-training methods that can improve long-horizon decision…
无需人工标注，通过神经元激活模式筛选数据，实现LLM高效自蒸馏训练。
arXiv:2607.02460v1 Announce Type: cross Abstract: Post-training large language models (LLMs) without real-world interaction feedback or human-labeled …
双层次自适应数据选择方法，大幅提升大模型训练效率与数据质量
arXiv:2606.18650v1 Announce Type: new Abstract: As Large Language Model (LLM) datasets scale to trillions of tokens, data selection has emerged as a c…
发现多数视觉指令样本可通过语言模式解决，提出无需训练的数据选择方法提升跨模态学习
arXiv:2603.09715v2 Announce Type: replace Abstract: Visual instruction tuning is crucial for improving vision-language large models (VLLMs). However, …
轻量级双层影响评分方法，高效筛选预训练数据，显著提升语言模型性能。
arXiv:2510.06048v4 Announce Type: replace Abstract: Effective data selection is essential for pretraining large language models (LLMs), enhancing effi…
最新研究揭示数据选择策略如何影响大模型微调的长期表现与鲁棒性。
arXiv:2605.30537v1 Announce Type: new Abstract: Data selection is increasingly used to reduce the cost of large language model (LLM) fine-tuning, with…
无需训练的多模态数据选择新方法，自剪枝机制高效筛选优质数据。
arXiv:2502.12119v4 Announce Type: replace-cross Abstract: Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to fol…
提出MIRA方法，通过中训练评分锚定实现来源感知数据选择，提升大模型训练质量。
arXiv:2605.30288v1 Announce Type: new Abstract: Mid-training has become an important stage in modern LLM development, using large-scale curated mixtur…
提出统一数据选择框架，为LLM推理任务高效筛选高质量训练数据，显著提升推理能力。
arXiv:2605.22389v1 Announce Type: new Abstract: Effectively training Large Language Models (LLMs) for complex, long-CoT reasoning is often bottlenecke…
提出强映射假设，揭示数据选择与参数高效微调的内在耦合，为LLM对齐提供新思路。
arXiv:2605.21558v1 Announce Type: cross Abstract: Adapting Large Language Models (LLMs) to specialized domains typically incurs high data and computat…
ICML 2026新方法：用重要性-多样性数据选择提升Web智能体跨域泛化能力
arXiv:2605.20291v1 Announce Type: new Abstract: Large language models (LLMs) have enabled web agents that follow natural language goals through multi-…
新方法用DPO隐式奖励差距衡量样本难度，自动筛选高质量偏好数据，提升模型训练效率。
arXiv:2508.04149v2 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with human preferences is a critical challenge in AI r…
提出Learning-Zone Energy方法，在线选择数据以提升RL后训练效率，避免均匀分配浪费计算。
arXiv:2605.17003v1 Announce Type: new Abstract: Reinforcement Learning (RL) post-training has emerged as the dominant paradigm for eliciting mathemati…
提出凸数据集估值方法，解决LLM后训练中数据集选择的成本与性能权衡问题
arXiv:2605.16704v1 Announce Type: new Abstract: Improving LLM performance on downstream tasks sometimes requires leveraging auxiliary datasets during …
论文提出即插即用的振荡式数据体积调度方法，超越传统样本选择，显著提升模型训练效率。
arXiv:2605.14773v1 Announce Type: cross Abstract: Data selection accelerates training by identifying representative training data while preserving mod…
由相似图构建加权独立集，平衡样本质量与多样性，为高效数据选择提供新框架。
arXiv:2605.15691v1 Announce Type: new Abstract: Data selection seeks to identify a compact yet informative subset from large-scale training corpora, b…