We introduce Reinforcement Learning (RL) with Adaptive Verifiable Environments (RLVE), an approach using verifiable environments that procedurally generate problems and provide algorithmically verifiable rewards, to scale up RL for language models (LMs). RLVE enables each verifiable environment to dynamically adapt its problem difficulty distribution to the policy model's capabilities as training progresses. In contrast, static data distributions often lead to vanishing learning signals when problems are either too easy or too hard for the policy. To implement RLVE, we create RLVE-Gym, a large-scale suite of 400 verifiable environments carefully developed through manual environment engineering. Using RLVE-Gym, we show that environment scaling, i.e., expanding the collection of training environments, consistently improves generalizable reasoning capabilities. RLVE with joint training across all 400 environments in RLVE-Gym yields a 3.37% absolute average improvement across six reasoning benchmarks, starting from one of the strongest 1.5B reasoning LMs. By comparison, continuing this LM's original RL training yields only a 0.49% average absolute gain despite using over 3x more compute. We release our code publicly.

翻译：我们提出了基于自适应可验证环境的强化学习（RLVE），该方法利用可验证环境程序化生成问题并提供算法可验证的奖励，以扩展语言模型（LMs）的强化学习规模。RLVE使每个可验证环境能够根据策略模型在训练过程中的能力动态调整其问题难度分布。相比之下，静态数据分布往往在问题对策略而言过于简单或过于困难时导致学习信号消失。为实现RLVE，我们创建了RLVE-Gym，这是一个通过人工环境工程精心开发的大规模套件，包含400个可验证环境。利用RLVE-Gym，我们证明环境扩展（即增加训练环境集合）能持续提升泛化推理能力。在RLVE-Gym中所有400个环境上进行联合训练的RLVE，从当前最强的1.5B参数推理语言模型之一出发，在六个推理基准测试中实现了3.37%的绝对平均提升。相比之下，延续该语言模型原有的强化学习训练仅获得0.49%的平均绝对增益，尽管计算量增加了三倍以上。我们已公开代码。