Towards General Long-Horizon Agents: Challenges and Innovations
Date:
Large language models are driving a paradigm revolution in artificial intelligence. From ChatGPT to Artificial General Intelligence (AGI), LLM-based agents have emerged as a highly promising technical direction. This talk first introduces the fundamental concepts of large models and agents, including the definition of AI agents, the core capabilities of LLM-based agents (perception and understanding, reasoning and planning, memory storage and retrieval, generation and action), as well as practical applications such as AI research assistants (WisPaper) and automated task execution. The talk then focuses on three major scaling challenges in building general long-horizon agents: Scaling Environments, Scaling Goals, and Scaling Interactions. Finally, we present our explorations and innovations on AgentGym, a cross-environment self-evolution framework, and AgentGym-RL, a reinforcement learning approach for long-horizon decision making.
