Training Language Agents to Learn from Experience
Researchers have developed a new framework called In-context Training (ICT) to enable language agents to learn and improve from past experiences across different tasks. This approach trains a "reflector" model to generate system prompts that enhance an "actor" model's performance on future, unseen tasks. Experiments in ALFWorld and MiniHack demonstrated that agents trained with this method showed improved performance on various task families, with some even generalizing to entirely new environments. AI
IMPACT Introduces a method for agents to generalize learning across tasks, potentially improving adaptability and efficiency in complex AI systems.