Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with chain-of-thought (CoT) prompting. However, LLMs sometimes still struggle with problems that are easy for humans, such as generating action plans to achieve given goals in an environment, or performing complex math or logical reasoning. The deficiency stems from the key fact that LLMs lack an internal world model to predict the world state (e.g., environment status, intermediate variable values) and simulate long-term outcomes of actions. This prevents LLMs from performing deliberate planning akin to human brains, which involves exploring alternative reasoning paths, anticipating future states and rewards, and iteratively refining existing reasoning steps. To overcome the limitations, we propose a new LLM reasoning framework, Reasoning via Planning (RAP). RAP repurposes the LLM as both a world model and a reasoning agent, and incorporates a principled planning algorithm based on Monte Carlo Tree Search for strategic exploration in the vast reasoning space. During reasoning, the LLM (as agent) incrementally builds a reasoning tree under the guidance of the LLM (as world model) and rewards, and efficiently obtains a high-reward reasoning path with a proper balance between exploration vs. exploitation. We apply RAP to various challenging reasoning problems including plan generation, math reasoning, and logical inference, and demonstrate its superiority over strong baselines. RAP with LLaMA-33B even surpasses CoT with GPT-4, achieving 33% relative improvement in a plan generation setting.
Insight:
Large language models (LLMs) have shown strong reasoning abilities, particularly when using techniques like Chain-of-Thought (CoT) prompting. However, they struggle with tasks that require strategic planning or predicting long-term outcomes, which are areas where humans excel due to an internal world model. This paper introduces a new framework called Reasoning via Planning (RAP) to overcome these limitations by combining LLMs with a world model and a planning algorithm to improve reasoning capabilities.
Proposed Approach:
The RAP framework integrates the LLM as both a reasoning agent and a world model. It uses Monte Carlo Tree Search (MCTS) for planning, allowing the model to simulate different reasoning paths and their outcomes. This approach balances exploration and exploitation to find the most effective reasoning path. The RAP framework is tested on various tasks, including plan generation, math reasoning, and logical inference, showing significant improvements over existing methods like CoT.
Alternative Approaches Previously Explored:
Previous methods, such as CoT prompting, decompose complex questions into sequential steps but often induce errors, especially as the number of steps increases. Other approaches include self-consistency methods that select the best answer through majority voting and least-to-most prompting, which breaks down tasks into simpler subquestions. These methods, however, do not integrate a world model or planning algorithm, limiting their ability to handle more complex reasoning tasks.
Limitations:
Dependency on LLM Quality: The effectiveness of RAP heavily depends on the quality of the underlying LLM. If the LLM has limitations, those are inherited by RAP.
Scalability Concerns: The MCTS-based approach, while powerful, can be computationally intensive, especially for larger, more complex reasoning tasks.
Complexity of Implementation: RAP's requirement for integrating a world model with MCTS increases the complexity of implementation compared to simpler methods like CoT.
Limited Generalization: The framework might not generalize well to all types of reasoning tasks, especially those outside the domains it was specifically tested on.
Experimental Results:
The RAP framework was tested across various reasoning tasks. For instance, in the Blocksworld problem, RAP achieved a 64% success rate, significantly outperforming CoT. Additionally, RAP using LLaMA-33B showed a 33% improvement over GPT-4 with CoT in plan generation tasks. The framework also showed consistent improvements in mathematical reasoning and logical inference tasks.
Future Research Directions:
Improving LLM Capabilities: Future work could focus on enhancing the underlying LLMs to improve RAP's overall performance.
Optimizing Planning Algorithms: Research could explore more efficient or alternative planning algorithms to reduce computational overhead while maintaining or improving performance.
Expanding Applicability: Extending RAP to a broader range of reasoning tasks and domains to test its generalization capabilities.
Integration with Real-World Applications: Investigating how RAP can be integrated into real-world applications, such as robotics or strategic decision-making, where planning and reasoning are critical.
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