REASONING-BASED APPROACHES

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Challenge:	Existing approaches to train large-scale models with extensive datasets are limited by their inadequate planning capabilities compared to humans.
Approach:	They propose a paradigm that enhances the performance of Large Language Models (LLMs) they use look-ahead planning to refine action selection and LEAN to streamline navigation through agile prompt construction.
Outcome:	The proposed framework outperforms agents trained via imitation learning, reinforcement learning, and reasoning-based approaches without any fine-tuning.

Papers with reasoning-based approaches