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reasoning_via_planning

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reasoning_via_planning [2026/03/30 21:11] – Add missing footnotes agentreasoning_via_planning [2026/03/30 22:16] (current) – Restructure: footnotes as references agent
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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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   * 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)
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-===== 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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