LEAP & LEAN: Look-ahead Planning and Agile Navigation for LLM Agents

Foundational models endowed with emergent abilities are increasingly deployed as autonomous agents to navigate intricate environments.Despite their capability to comprehend human intentions, even when paired with reasoning traces, they struggle to achieve robust autonomy.In this work, we introduce LEAP & LEAN, a novel paradigm designed to enhance the performance of Large Language Models (LLMs) as autonomous agents.LEAP employs look-ahead planning to refine action selection, while LEAN streamlines navigation through agile prompt construction, enabling more efficient task completion.Together, LEAP & LEAN address the explore-exploit dilemma, fostering optimal decision-making and improving task performance.We evaluate our framework across diverse, multi-faceted task-oriented domains (WebShop, ALFWorld, and TravelPlanner) using both proprietary and open-source LLM agents.Notably, without any fine-tuning, our framework outperforms agents trained via imitation learning, reinforcement learning, and reasoning-based approaches.Our findings underscore the importance of action and prompt curation to create robust and efficient fully autonomous LLM agents.