Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising approach for training reasoning language models (RLMs) by leveraging supervision from verifiers. Although verifier implementation is easier than solution annotation for many tasks, existing synthetic data generation methods remain largely solution-centric, while verifier-based methods rely on a few hand-crafted procedural environments. In this work, we scale RLVR by introducing ReSyn, a pipeline that generates diverse reasoning environments equipped with instance generators and verifiers, covering tasks such as constraint satisfaction, algorithmic puzzles, and spatial reasoning. A Qwen2.5-7B-Instruct model trained with RL on ReSyn data achieves consistent gains across reasoning benchmarks and out-of-domain math benchmarks, including a 27\% relative improvement on the challenging BBEH benchmark. Ablations show that verifier-based supervision and increased task diversity both contribute significantly, providing empirical evidence that generating reasoning environments at scale can enhance reasoning abilities in RLMs
DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data
Computational Science Research Center, San Diego State University（圣地亚哥州立大学计算科学研究中心）；School of Nursing and Department of Computer Science, University of California, Irvine（加州大学尔湾分校护理学院与计算机科学系）；Fowler College of Business, San Diego State University（圣地亚哥州立大学福勒商学院）；Department of Computer Science, San Diego State University（圣地亚哥州立大学计算机科学系）
Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning
The University of British Columbia（英属哥伦比亚大学）；Fudan University（复旦大学）；Royal College of Science, Imperial College London（伦敦帝国理工学院皇家科学学院）；Dyson School of Design Engineering（戴森设计工程学院）；Suzhou Institute of Biomedical Engineering and Technology (SIBET), Chinese Academy of Sciences（中国科学院苏州生物医学工程技术研究所）；East China Normal University（华东师范大学）
