Microsoft launches XOT technology to enhance the reasoning capabilities of language models

November 15 news, Microsoft recently launched a method called "Everything of Thought" (XOT), inspired by Google DeepMind's AlphaZero, Using compact neural networks , to enhance the reasoning ability of the AI ​​model.

##Microsoft collaborated with Georgia Institute of Technology and East China Normal University to develop the algorithm , integrating reinforcement learning and Monte Carlo Tree Search (MCTS) capabilities to further improve the effectiveness of problem solving in complex decision-making environments.

Note from this site: The Microsoft research team stated that the XOT method can expand the language model on unfamiliar problems, and has been significantly improved in rigorous tests of Game of 24, 8-Puzzle and Pocket Cube. The results show that XOT is significantly better than other methods and even solves the problem where other methods fail. However, XOT does not achieve 100% reliability

#The XOT framework includes the following key steps:

Pre-training phase: MCTS module Pre-training on tasks to learn domain knowledge about effective mental search. Lightweight strategy and value networks guide search. Idea Search: During inference, the pre-trained MCTS module uses a policy/value network to efficiently explore and generate LLM’s idea trajectories.
• Thought Correction: The LLM reviews MCTS’ thoughts and identifies any errors. Ideas for revisions were generated through additional MCTS simulations.
• LLM Reasoning: Provide revised ideas to LLM for final tips on problem solving.

This website attaches the address of the paper [

PDF], and interested users can read it in depth.

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