mlsanityData to 7 Oct 2026

Cooperative Strategic Planning Enhances Reasoning Capabilities in Large Language Models

arXiv 2410.20007 ↗First posted 25 Oct 2024cs.AI cs.CLcited by 6

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

Enhancing the reasoning capabilities of large language models (LLMs) is crucial for enabling them to tackle complex, multi-step problems. Multi-agent frameworks have shown great potential in enhancing LLMs' reasoning capabilities. However, the lack of effective cooperation between LLM agents hinders their performance, especially for multi-step reasoning tasks. This paper proposes a novel cooperative multi-agent reasoning framework (CoPlanner) by separating reasoning steps and assigning distinct duties to different agents. CoPlanner consists of two LLM agents: a planning agent and a reasoning agent. The planning agent provides high-level strategic hints, while the reasoning agent follows these hints and infers answers. By training the planning agent's policy through the interactive reasoning process via Proximal Policy Optimization (PPO), the LLaMA-3-8B-based CoPlanner outperforms the previous best method by 9.94% on LogiQA and 3.09% on BBH. Our results demonstrate that the guidance from the planning agent and the effective cooperation between the agents contribute to the superior performance of CoPlanner in tackling multi-step reasoning problems.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 24 of its 31 references that are in the map. First posted on arXiv 25 Oct 2024.

Builds on

24 of 31 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?28 Feb
  2. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May
  3. 2023Reasoning with Language Model is Planning with World Model24 May

Other references · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  5. 2023Let's Verify Step by Stepcited by 3,598
  6. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  7. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Themcited by 1,767
  8. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
  9. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 965
  10. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 873
  11. 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
  12. 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 238
9 more
  1. 2023PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimizationcited by 226
  2. 2020Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learningcited by 216
  3. 2023Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Gamecited by 148
  4. 2023War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Warscited by 143
  5. 2023RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputscited by 104
  6. 2023Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communicationcited by 78
  7. 2022Solving Math Word Problems via Cooperative Reasoning induced Language Modelscited by 76
  8. 2024Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debatecited by 40
  9. 2024TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidancecited by 5

Built on by

Cited by year

Citing papers per year: 2025: 6.

6 papers.

  1. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 381
  2. 2025K2-Think: A Parameter-Efficient Reasoning Systemcited by 11
  3. 2025EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learningcited by 8
  4. 2025Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanismcited by 4
  5. 2025Rethinking the Illusion of Thinkingcited by 2
  6. 2025Alternatives To Next Token Prediction In Text Generation -- A Surveycited by 1

Similar, not cited

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  1. 2026Foresight Optimization for Strategic Reasoning in Large Language Models
  2. 2024LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models
  3. 2024STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making
  4. 2024TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
  5. 2023Strategic Reasoning with Language Models
  6. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey
  7. 2024K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning
  8. 2024Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions