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GEM: A Gym for Agentic LLMs
arXiv – CS AI|Zichen Liu, Anya Sims, Keyu Duan, Changyu Chen, Simon Yu, Xiangxin Zhou, Haotian Xu, Shaopan Xiong, Bo Liu, Chenmien Tan, Chuen Yang Beh, Weixun Wang, Hao Zhu, Weiyan Shi, Diyi Yang, Michael Shieh, Yee Whye Teh, Wee Sun Lee, Min Lin||3 views
🤖AI Summary
Researchers introduced GEM (General Experience Maker), an open-source environment simulator designed for training large language models through experience-based learning rather than static datasets. The framework provides a standardized interface similar to OpenAI-Gym but specifically optimized for LLMs, featuring diverse environments, integrated tools, and compatibility with popular RL training frameworks.
Key Takeaways
- →GEM is a new open-source framework that enables LLMs to learn through environmental interaction rather than static training data.
- →The platform provides standardized interfaces, asynchronous vectorized execution, and flexible wrappers for easy extensibility.
- →GEM includes baselines across 24 environments and supports five popular reinforcement learning training frameworks.
- →The framework introduces REINFORCE with Return Batch Normalization (ReBN) which offers better credit assignment than existing methods.
- →GEM functions both as a training environment and evaluation toolkit for agentic LLM research.
#llm#reinforcement-learning#open-source#ai-training#machine-learning#agentic-ai#environment-simulator#research-framework
Read Original →via arXiv – CS AI
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