Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain informative as models evolve. In practice, RL progress often slows when task difficulty becomes poorly aligned with model capability, or when training is dominated by a narrow set of recurring problem patterns. To jointly address these issues, we propose SCALER (Synthetic sCalable Adaptive Learning Environment for Reasoning), a framework that sustains effective learning signals through adaptive environment design. SCALER introduces a scalable synthesis pipeline that converts real-world programming problems into verifiable reasoning environments with controllable difficulty and unbounded instance generation, enabling RL training beyond finite datasets while preserving strong correctness guarantees. Building on this, SCALER further employs an adaptive multi-environment RL strategy that dynamically adjusts instance difficulty and curates the active set of environments to track the model's capability frontier and maintain distributional diversity. This co-adaptation prevents reward sparsity, mitigates overfitting to narrow task patterns, and supports sustained improvement throughout training. Extensive experiments show that SCALER consistently outperforms dataset-based RL baselines across diverse reasoning benchmarks and exhibits more stable, long-horizon training dynamics.
LLM-based Automated Theorem Proving Hinges on Scalable Synthetic Data Generation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术全国重点实验室）；School of Computing and Mathematical Sciences, Birkbeck, University of London（伦敦大学伯贝克学院计算与数学科学学院）
SMART: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining
Institute of Artificial Intelligence, China Telecom（中国电信人工智能研究院）；Technical University of Munich（慕尼黑工业大学）；Harbin Institute of Technology（哈尔滨工业大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）
Optimizing Agentic Reasoning with Retrieval via Synthetic Semantic Information Gain Reward
Hong Kong JC STEM Lab of Smart City.（香港JC STEM实验室）；City University of Hong Kong.（香港城市大学）；Lingnan University（岭南大学）；Fudan University.（复旦大学）；Huazhong University of Science and Technology.（华中科技大学）
Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment
Snowflake AI Research（Snowflake人工智能研究院）；Massachusetts General Hospital（麻省总医院）；AGH University of Krakow（克拉科夫AGH大学）；Massachusetts Institute of Technology（麻省理工学院）；King’s College London（伦敦国王学院）；Harvard Medical School（哈佛医学院）
Scalable AI Framework for Defect Detection in Metal Additive Manufacturing
Polaron Analytics（极化子分析公司）；University of Dayton Research Institute（代顿大学研究院）；University of Dayton（代顿大学）；NASA Marshall Space Flight Center（美国国家航空航天局马歇尔太空飞行中心）；National Aeronautics and Space Administration（美国国家航空航天局）
