nano_rl is a secondary development project based on verl, positioned as custom development of reward on the verl framework.
Purpose The project aims to enhance the flexibility and performance of the original verl framework, mainly optimizing the invocation and management of reward models.
Main capabilities According to the project materials, nano_rl centers on custom development of reward models, covering implementation methods for custom reward models as well as related practices for conducting Agent training in verl. The specific content mentioned in the materials includes: how to customize reward models in verl; practice of custom reward models; lessons learned from pitfalls in verl Agent training; practice of Agent SFT and RL training in review scenarios.
Usage Specific installation commands and usage steps are not yet provided. The project is released in the form of a code repository, at https://github.com/yuanzhoulvpi2017/nano_rl .
Applicable scenarios The materials indicate that it involves scenarios such as reward model customization, Agent training, SFT and RL training, especially the practice of Agent SFT and RL training in review scenarios.
Limitations The project materials do not provide pricing, platform support, performance data, or effectiveness explanations, and such related information is not yet available.
