Dynamic Data Selection for Curriculum Learning via Ability Estimation
Department of IT, Analytics, and Operations, University of Notre Dame（圣母大学IT、分析与运营系）；Department of Computer Science, University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校计算机科学系）；College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）
Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty heuristics with learned difficulty parameters. We also propose Dynamic Data selection for Curriculum Learning via Ability Estimation (DDaCLAE), a strategy that probes model ability at each training epoch to select the best training examples at that point. We show that models using learned difficulty and/or ability outperform heuristic-based curriculum learning models on the GLUE classification tasks.
LyriCAR: A Difficulty-Aware Curriculum Reinforcement Learning Framework For Controllable Lyric Translation
School of Informatics, Xiamen University（厦门大学信息学院）；School of Journalism and Communication, Xiamen University（厦门大学新闻传播学院）；School of Film, Xiamen University（厦门大学电影学院）；Xiamen Key Laboratory of Intelligent Storage and Computing, Xiamen University（厦门大学智能存储与计算重点实验室）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）
Actor-Curator: Co-adaptive Curriculum Learning via Policy-Improvement Bandits for RL Post-Training
University of Illinois Chicago（伊利诺伊大学香槟分校）；Caltech（加州理工学院）；RPI（罗切斯特理工学院）；MBZUAI（澳门大学人工智能研究院）；University of Chicago（芝加哥大学）；NEC Laboratories America（NEC美国实验室）
TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models
Electronics and Telecommunications Research Institute（电信研究所）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Hugging Face；Ant Group（蚂蚁集团）
A Psychology-based Unified Dynamic Framework for Curriculum Learning
University of Notre Dame, Department of Computer Science and Engineering（诺丁汉大学计算机科学与工程系）；Nankai University, College of Computer Science（南开大学计算机学院）；University of Notre Dame, Department of IT, Analytics, and Operations（诺丁汉大学信息技术、分析与运营系）；VA Bedford Healthcare System, Center for Health Optimization and Implementation Research（美国退伍军人事务部贝福德医疗系统健康优化与实施研究中心）；University of Massachusetts Amherst, Manning College of Information and Computer Sciences（马萨诸塞大学阿姆赫斯特分校曼宁信息与计算机科学学院）；University of Massachusetts Lowell, Miner School of Computer and Information Sciences（马萨诸塞大学洛威分校米纳尔计算机与信息科学学院）
RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Alibaba Group（阿里巴巴集团）；School of Biomedical Engineering, UNSW Sydney（新南威尔士大学生物医学工程学院）
DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generation
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning
School of Information, Renmin University of China（中国人民大学信息学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学歌华人工智能学院）；Alibaba Group（阿里巴巴集团）
Tailored Teaching with Balanced Difficulty: Elevating Reasoning in Multimodal Chain-of-Thought via Prompt Curriculum
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Hunan Vanguard Group Corporation Co., Ltd.（湖南 Vanguard集团有限公司）；Huaneng Information Technology Co., Ltd.（华能信息技术有限公司）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；PowerChina Huadong Engineering Co., Ltd.（中国电建华东工程有限公司）
