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DIFFUSEARCH: Revolutionizing Chess AI with Implicit Search and Discrete Diffusion Modeling
Large Language Models (LLMs) have gained significant attention in AI research due to their impressive capabilities. However, their limitation lies with long-term planning and complex problem-solving. While explicit search methods like Monte Carlo Tree Search (MCTS) have been employed to enhance decision-making in various AI systems, including chess engines and game-playing algorithms, they present challenges when applied to LLMs. The recursive use of value models during searching leads to error accumulation and increased computational costs, especially for long-horizon tasks. So, it is necessary to enable LLMs to predict and utilize future information without depending on explicit search methods, aiming to
Large Language Models (LLMs) have gained significant attention in AI research due to their impressive capabilities. However, their limitation lies with long-term planning and complex problem-solving. While explicit search methods like Monte Carlo Tree Search (MCTS) have been employed to enhance decision-making in various AI systems, including chess engines and game-playing algorithms, they present challenges when applied to LLMs. The recursive use of value models during searching leads to error accumulation and increased computational costs, especially for long-horizon tasks. So, it is necessary to enable LLMs to predict and utilize future information without depending on explicit search methods, aiming to
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