📄 中文摘要
该研究提出了SimpleRL-Zoo，一个用于零强化学习的框架，旨在为开放基础模型提供更好的适应性和性能。通过对多种环境的实验，验证了该框架在不同任务中的有效性。研究中引入了一系列新的技术和方法，以优化学习过程并提高模型的泛化能力。结果表明，SimpleRL-Zoo在处理复杂任务时表现出色，能够有效地应对现实世界中的挑战。该框架的设计理念和实现细节为未来的研究提供了重要的参考。
SimpleRL-Zoo：研究和驯化零强化学习以适应开放基础模型
📄 English Summary
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open BaseModels in the Wild
The study introduces SimpleRL-Zoo, a framework for zero reinforcement learning aimed at enhancing adaptability and performance for open base models. Experiments across various environments validate the framework's effectiveness in different tasks. A series of new techniques and methods are introduced to optimize the learning process and improve the model's generalization capabilities. Results demonstrate that SimpleRL-Zoo excels in handling complex tasks, effectively addressing challenges in real-world scenarios. The design philosophy and implementation details of the framework provide significant references for future research.