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  "documentTitle": "2023 Air Street Capital The State of AI Report 2023",
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  "notes": "The slide contrasts standard Chain of Thought (linear) with RAP (tree-based planning).",
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      "text": "Reasoning via Planning (RAP) uses Monte Carlo Tree Search to find a high-reward reasoning path efficiently.",
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      "text": "The world model can generate an action as well as predict the next state reached by taking that action. This produces a reasoning trace which makes the LM more coherent then Chain of Thought methods which predict next actions but not next world states.\nThe rewards are also obtained from the LM and used to maintain a state-action value function for planning with MCTS.\nWhile being significantly more expensive, RAP outperforms Chain-of-Thought reasoning approaches on plan generation, math reasoning and logical reasoning. RAP on LLaMA-33B even outperforms CoT on GPT-4 in a setting of Blocksworld.",
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      "text": "Reasoning has been traditionally thought of as searching a space of possible outcomes and picking the best one. By containing so much information about the world, LLMs offer the opportunity of generating this space (often called a world model) in which planning algorithms can explore. Reasoning via Planning (RAP) uses Monte Carlo Tree Search to find a high-reward reasoning path efficiently.",
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