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LLM Reasonsers is a library to enable LLMs to conduct complex reasoning, with advanced reasoning algorithms. It approaches multi-step reasoning as planning and searches for the optimal reasoning chain, and achieves the best balance of exploration vs exploitation with the idea of "World Model" and "Reward".

Given any reasoning problem, simply define the reward function and an optional world model, and let LLM reasoners take care of the rest, including Search Algorithms, Visualization, LLM calling, and more!

Visualization

Our library provides visualization tools to aid users in comprehending the reasoning process. Even for the most complex reasoning algorithms like Monte-Carlo Tree Search, users can easily diagnose and understand what occurred with one line of python code. Here we show some examples from GSM8K and Blocksworld.

My goal is to have have that the orange block is on top of the blue block. The initial state is that, the red block is clear, the blue block is clear, the yellow block is clear, the hand is empty, the blue block is on top of the orange block, the red block is on the table, the orange block is on the table and the yellow block is on the table. What's the action plan to achieve my goal?
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