RL- and LLM-Based AI Solvers for the Game of Wordle/Fibble

This paper investigates the performance of reinforcement learning (RL)-based AI agents and large language model (LLM)-based AI agents in tackling the word-guessing game Wordle and its variant, Fibble, which introduces misleading feedback. We conducted experiments to compare a range of RL-based approaches and several LLM chatbots, revealing distinct performance differences, especially when confronted with deceptive clues. Our findings show that an RL algorithm specifically designed to handle misleading information outperformed others in Fibble, while a standard RL algorithm designed for the original Wordle excelled in that game but struggled with Fibble. LLM-based solvers demonstrated reasonable performance across both games but generally fell short of the effectiveness of RL-based solvers.