GEM: A Gym for Agentic LLMs
Authors: name: Zichen Liu, name: Anya Sims, name: Keyu Duan, name: Changyu Chen, name: Simon Yu, name: Xiangxin Zhou, name: Haotian Xu, name: Shaopan Xiong, name: Bo Liu, name: Chenmien Tan, name: Chuen Yang Beh, name: Weixun Wang, name: Hao Zhu, name: Weiyan Shi, name: Diyi Yang, name: Michael Shieh, name: Yee Whye Teh, name: Wee Sun Lee, name: Min Lin
Organization: Sea AI Lab

The training paradigm for large language models (LLMs) is moving from static
datasets to experience-based learning, where agents acquire skills via
interacting with complex environments. To facilitate this transition we
introduce GEM (General Experience Maker), an open-source environment simulator
designed for the age of LLMs. Analogous to OpenAI-Gym for traditional
reinforcement learning (RL), GEM provides a standardized framework for the
environment-agent interface, including asynchronous vectorized execution for
high throughput, and flexible wrappers for easy extensibility. GEM also
features a diverse suite of environments, robust integrated tools, and
single-file example scripts demonstrating using GEM with five popular RL
training frameworks. Along with this, we also provide a set of baselines across
24 environments using REINFORCE with Return Batch Normalization (ReBN), which
-- unlike GRPO -- is compatible with the full RL setting of dense per-turn
rewards and offers better credit assignment. We further conduct apple-to-apple
benchmarking of PPO, GRPO and REINFORCE in both single- and multi-turn settings
using GEM to shed light on the algorithmic designs. Lastly, GEM also functions
as a convenient evaluation toolkit besides a training environment. We hope this
framework can help accelerate future agentic LLM research.