Curriculum Learning for Embodied Planning with LLMs

This project explores the application of Curriculum Learning to improve the performance of GPT-2 models in Embodied Natural Language Processing tasks using the ALFWorld dataset. We developed curricula for both Action Modeling and Reinforcement Learning stages, demonstrating significant improvements in task success rates and action efficiency.

Authors: Bohan Liu, Vedaant Jain, Aarohi Gupta

Key aspects of this research include:

• Developing difficulty scoring mechanisms for task demonstrations
• Creating “Easy” and “Hard” curriculum sets to structure model training
• Investigating the impact of curricula on model generalization across task types
• Exploring the potential of few-shot learning with large language models
• Demonstrating the effectiveness of a two-stage “easy-then-hard” curriculum in Reinforcement Learning

Our results show that carefully designed curricula can enhance model performance, improve generalization to unseen tasks, and increase learning efficiency in embodied AI environments.