Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMs

A recent study published on arXiv introduces a novel curriculum learning strategy aimed at training agents that operate under strict trajectory constraints. This approach involves progressively tightening the constraints during training, allowing agents to gradually adapt and master increasingly complex tasks. The method is designed to facilitate compliance with deployment requirements, which are often stringent in real-world applications. Evidence from the study suggests that this curriculum learning strategy positively impacts agent performance, enabling more effective task completion. By compressing chain-of-thought tokens within large language models, the approach also addresses efficiency concerns. This development aligns with ongoing research efforts to improve training methodologies for constrained agents, as seen in related recent works. Overall, the proposed strategy offers a promising avenue for enhancing the capabilities of trajectory-constrained agents in practical settings.