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reasoning_via_planning [2026/03/30 21:02] – Add inline footnotes agentreasoning_via_planning [2026/03/30 22:16] (current) – Restructure: footnotes as references agent
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 ====== RAP: Reasoning via Planning with LLM as World Model ====== ====== RAP: Reasoning via Planning with LLM as World Model ======
  
-**RAP** (Reasoning via Planning) is a framework introduced by Hao et al. (2023) that repurposes a large language model to serve as **both a world model and a reasoning agent**, guided by **Monte Carlo Tree Search (MCTS)** to explore high-reward reasoning paths.((https://arxiv.org/abs/2305.14992)) With **925 citations**, it demonstrates that deliberate planning significantly outperforms autoregressive chain-of-thought reasoning across diverse tasks by enabling strategic exploration, lookahead, and backtracking.+**RAP** (Reasoning via Planning) is a framework introduced by Hao et al. (2023) that repurposes a large language model to serve as **both a world model and a reasoning agent**, guided by **Monte Carlo Tree Search (MCTS)** to explore high-reward reasoning paths.((https://arxiv.org/abs/2305.14992)) With **925 citations**, it demonstrates that deliberate planning significantly outperforms autoregressive chain-of-thought reasoning(([[https://arxiv.org/abs/2201.11903|Wei et al. "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" (2022)]])) across diverse tasks by enabling strategic exploration, lookahead, and backtracking.
  
 [[https://arxiv.org/abs/2305.14992|arXiv:2305.14992]] [[https://arxiv.org/abs/2305.14992|arXiv:2305.14992]]
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   - **Backpropagation**: Update value estimates along the traversed path   - **Backpropagation**: Update value estimates along the traversed path
  
-The final answer is selected from the highest-reward complete reasoning trace, optionally aggregated via majority vote across multiple MCTS runs.+The final answer is selected from the highest-reward complete reasoning trace, optionally aggregated via majority vote across multiple MCTS runs(([[https://arxiv.org/abs/2203.11171|Wang et al. "Self-Consistency Improves Chain of Thought Reasoning" (2022)]])).
  
 ===== System Architecture ===== ===== System Architecture =====
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 | Logical Reasoning | Hypothesis verification | Outperforms with detailed proofs | | Logical Reasoning | Hypothesis verification | Outperforms with detailed proofs |
  
-  * MCTS enables strategic exploration that autoregressive CoT cannot achieve((https://arxiv.org/abs/2305.14992))+  * MCTS enables strategic exploration that autoregressive CoT cannot achieve(([[https://aclanthology.org/2023.emnlp-main.507|EMNLP 2023 Proceedings]]))((https://arxiv.org/abs/2305.14992))
   * World model allows anticipating consequences before committing to a reasoning step   * World model allows anticipating consequences before committing to a reasoning step
   * Scales effectively: more MCTS iterations yield better reasoning quality   * Scales effectively: more MCTS iterations yield better reasoning quality
   * Compatible with any LLM backbone (tested on text-davinci-002/003)   * Compatible with any LLM backbone (tested on text-davinci-002/003)
- 
-===== References ===== 
- 
-  * [[https://arxiv.org/abs/2305.14992|Hao et al. "Reasoning with Language Model is Planning with World Model" (2023)]] 
-  * [[https://aclanthology.org/2023.emnlp-main.507|EMNLP 2023 Proceedings]] 
-  * [[https://arxiv.org/abs/2201.11903|Wei et al. "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models" (2022)]] 
-  * [[https://arxiv.org/abs/2203.11171|Wang et al. "Self-Consistency Improves Chain of Thought Reasoning" (2022)]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[toolllm|ToolLLM: Mastering 16,000+ Real-World APIs]]   * [[toolllm|ToolLLM: Mastering 16,000+ Real-World APIs]]
   * [[expel_experiential_learning|ExpeL: Experiential Learning Agents]]   * [[expel_experiential_learning|ExpeL: Experiential Learning Agents]]
 +
 +===== References =====
  
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