LLM-Modulo Framework: Harnessing the Power of Language Models for Efficient Planning and Reasoning

The rapid advancements in Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling machines to generate human-like text with remarkable coherence and fluency. However, despite their impressive linguistic abilities, the question remains: Can LLMs truly plan and reason like humans? A groundbreaking research paper by Subbarao Kambhampati and his team delves into this critical question, proposing a novel approach called the "LLM-Modulo Framework" to effectively leverage LLMs for planning and reasoning tasks.

The Limitations of LLMs in Autonomous Planning and Reasoning

While LLMs have demonstrated remarkable performance in various language-related tasks, the researchers argue that they cannot independently plan or self-verify. This limitation arises from the fundamental nature of their training and operation. LLMs generate text based on patterns learned from vast amounts of training data, relying on a process of pattern matching and intuitive processing. However, this approach differs significantly from the systematic reasoning required for planning tasks.


The paper highlights the distinction between the fast, intuitive processing of LLMs, akin to a pseudo-System 1, and the slow, deliberate, and logical thinking associated with System 2 competencies, which are typically required for planning and reasoning. This dichotomy underscores the challenges in expecting LLMs to autonomously perform complex planning and reasoning tasks.

Misinterpretations in existing literature

The researchers point out that many existing papers claiming LLMs possess planning and reasoning abilities often suffer from misunderstandings or oversimplifications. Some studies test LLMs in domains that disregard interactions between subgoals, presenting an oversimplified view of planning. Others rely heavily on human intervention through prompting to correct and refine the generated plans, masking the true limitations of LLMs in autonomous planning.

LLM modulo framework
Experimental evidence revealing LLM limitations

To support their position, the authors conducted rigorous experiments evaluating the performance of state-of-the-art LLMs, such as GPT-4, on various planning tasks. The results were sobering, with only an average of 12% of the plans generated by the best-performing LLMs being fully correct and executable. Fine-tuning the models did not yield significant improvements, and when the names of actions and objects in the planning domain were obfuscated, the performance further deteriorated. These findings suggest that LLMs are more likely retrieving plans based on surface-level similarities rather than engaging in genuine planning.


The researchers also investigated the ability of LLMs to verify the correctness of plans and improve through self-critique. Again, the results were discouraging, with LLMs performing no better at verifying solutions than generating them. Having LLMs critique their own plans did not lead to meaningful enhancements in plan quality.

The LLM-Modulo Framework: combining LLMs with external critics

While the findings highlight the limitations of LLMs in autonomous planning and reasoning, the researchers emphasize that this does not render LLMs useless for these tasks. Instead, they introduce the LLM-Modulo Framework as a means to productively harness the strengths of LLMs.
The core idea behind the LLM-Modulo Framework is to integrate the generative capabilities of LLMs with external "critics" or verifiers. In this framework, LLMs serve as idea generators, producing candidate plans and ideas. These candidates are then scrutinized by a bank of specialized critics, which evaluate the plans based on various criteria, including hard constraints like executability and soft constraints like style and user preferences.

The framework incorporates model-based critics to ensure the soundness and correctness of the plans, relying on formal domain models and planning algorithms for validation. Additionally, LLM-based critics can be employed to assess softer aspects like style and coherence.
The critics provide feedback to the LLM, guiding it in refining and improving the generated plans iteratively. This feedback loop enables the LLM to learn from its mistakes and generate higher-quality plans over time.

Multiple roles of LLMs in the framework

The LLM-Modulo Framework allows LLMs to assume multiple roles in the planning and reasoning process:

  • Plan Generation: LLMs can generate candidate plans based on the problem specification and previous feedback from the critics.
  • Format Conversion: LLMs excel at converting information between different formats, enabling them to translate the generated plans into representations interpretable by various critics.
  • Problem Specification Assistance: LLMs can assist users in refining problem specifications by asking clarifying questions and suggesting improvements.
  • Model Acquisition: LLMs can aid in acquiring the domain models used by the model-based critics, extracting relevant information from text and engaging in dialogue with domain experts to refine the models.

Human Involvement in the Framework

While the LLM-Modulo Framework aims to automate much of the planning and reasoning process, human involvement remains crucial in certain aspects. Domain experts play a role in acquiring and refining the domain models used by the model-based critics, while end users are involved in refining the problem specifications through interaction with the LLM.
However, the framework seeks to minimize the need for human intervention in the time-consuming task of iterative plan critiquing. By automating the feedback loop between the LLM and the critics, the framework enables efficient and scalable plan generation and refinement.

Case studies demonstrating the framework's effectiveness

The researchers applied the LLM-Modulo Framework to several planning domains to showcase its effectiveness. In the Blocks World domain, a classic planning benchmark, the performance of the LLM improved to an impressive 82% within 15 feedback rounds from a model-based verifier. This highlights the framework's ability to guide the LLM towards generating high-quality plans through iterative refinement.
In a more complex travel planning task, the LLM-Modulo Framework achieved a remarkable 6 times better performance compared to baseline approaches. By leveraging the generative power of the LLM and the rigorous validation of the critics, the framework successfully generated coherent and executable travel plans that satisfied various constraints and preferences.

The potential and promise of the LLM-Modulo framework

The research paper by Kambhampati and his team sheds light on the capabilities and limitations of LLMs in planning and reasoning tasks. While LLMs cannot autonomously plan or self-verify, they can still play a productive role when combined with external verifiers in frameworks like LLM-Modulo.
The LLM-Modulo Framework harnesses the strengths of LLMs in generating candidate plans and ideas while ensuring the correctness and soundness of the plans through model-based critics. By automating the feedback loop and minimizing the need for human intervention, the framework enables efficient and scalable planning and reasoning.
The case studies demonstrate the potential of the LLM-Modulo Framework to extend the scope of planning to more flexible and expressive problem specifications. By combining the generative power of LLMs with the rigor of symbolic planning techniques, the framework offers a promising direction for tackling complex real-world planning and reasoning challenges.


As the field of artificial intelligence continues to evolve, the LLM-Modulo Framework serves as a testament to the importance of combining the strengths of different approaches to achieve robust and reliable planning and reasoning. By harnessing the power of LLMs and integrating them with symbolic planning techniques, researchers and practitioners can push the boundaries of what is possible in AI-driven problem-solving.


The acceptance of the research paper for a spotlight presentation at the prestigious International Conference on Machine Learning (ICML) 2024 further underscores the significance and impact of the LLM-Modulo Framework. The spotlight presentation and the accompanying tutorial session provide an excellent opportunity for the authors to share their insights and engage with the broader machine learning community.
As we look towards the future, the LLM-Modulo Framework offers a promising path for leveraging the power of language models in planning and reasoning tasks. By continuing to explore and refine this approach, researchers can unlock new frontiers in AI-driven problem-solving, paving the way for more intelligent and adaptive systems that can tackle complex real-world challenges.

Link to the paper: https://arxiv.org/abs/2402.01817

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5e650feec4ad5bfc4d0c19a.css" as="style" media="all"> LLM-Modulo Framework: Harnessing the Power of Language Models for Efficient Planning and Reasoning

LLM-Modulo Framework: Harnessing the Power of Language Models for Efficient Planning and Reasoning

The rapid advancements in Large Language Models (LLMs) have revolutionized the field of natural language processing, enabling machines to generate human-like text with remarkable coherence and fluency. However, despite their impressive linguistic abilities, the question remains: Can LLMs truly plan and reason like humans? A groundbreaking research paper by Subbarao Kambhampati and his team delves into this critical question, proposing a novel approach called the "LLM-Modulo Framework" to effectively leverage LLMs for planning and reasoning tasks.

The Limitations of LLMs in Autonomous Planning and Reasoning

While LLMs have demonstrated remarkable performance in various language-related tasks, the researchers argue that they cannot independently plan or self-verify. This limitation arises from the fundamental nature of their training and operation. LLMs generate text based on patterns learned from vast amounts of training data, relying on a process of pattern matching and intuitive processing. However, this approach differs significantly from the systematic reasoning required for planning tasks.


The paper highlights the distinction between the fast, intuitive processing of LLMs, akin to a pseudo-System 1, and the slow, deliberate, and logical thinking associated with System 2 competencies, which are typically required for planning and reasoning. This dichotomy underscores the challenges in expecting LLMs to autonomously perform complex planning and reasoning tasks.

Misinterpretations in existing literature

The researchers point out that many existing papers claiming LLMs possess planning and reasoning abilities often suffer from misunderstandings or oversimplifications. Some studies test LLMs in domains that disregard interactions between subgoals, presenting an oversimplified view of planning. Others rely heavily on human intervention through prompting to correct and refine the generated plans, masking the true limitations of LLMs in autonomous planning.

LLM modulo framework
Experimental evidence revealing LLM limitations

To support their position, the authors conducted rigorous experiments evaluating the performance of state-of-the-art LLMs, such as GPT-4, on various planning tasks. The results were sobering, with only an average of 12% of the plans generated by the best-performing LLMs being fully correct and executable. Fine-tuning the models did not yield significant improvements, and when the names of actions and objects in the planning domain were obfuscated, the performance further deteriorated. These findings suggest that LLMs are more likely retrieving plans based on surface-level similarities rather than engaging in genuine planning.


The researchers also investigated the ability of LLMs to verify the correctness of plans and improve through self-critique. Again, the results were discouraging, with LLMs performing no better at verifying solutions than generating them. Having LLMs critique their own plans did not lead to meaningful enhancements in plan quality.

The LLM-Modulo Framework: combining LLMs with external critics

While the findings highlight the limitations of LLMs in autonomous planning and reasoning, the researchers emphasize that this does not render LLMs useless for these tasks. Instead, they introduce the LLM-Modulo Framework as a means to productively harness the strengths of LLMs.
The core idea behind the LLM-Modulo Framework is to integrate the generative capabilities of LLMs with external "critics" or verifiers. In this framework, LLMs serve as idea generators, producing candidate plans and ideas. These candidates are then scrutinized by a bank of specialized critics, which evaluate the plans based on various criteria, including hard constraints like executability and soft constraints like style and user preferences.

The framework incorporates model-based critics to ensure the soundness and correctness of the plans, relying on formal domain models and planning algorithms for validation. Additionally, LLM-based critics can be employed to assess softer aspects like style and coherence.
The critics provide feedback to the LLM, guiding it in refining and improving the generated plans iteratively. This feedback loop enables the LLM to learn from its mistakes and generate higher-quality plans over time.

Multiple roles of LLMs in the framework

The LLM-Modulo Framework allows LLMs to assume multiple roles in the planning and reasoning process:

  • Plan Generation: LLMs can generate candidate plans based on the problem specification and previous feedback from the critics.
  • Format Conversion: LLMs excel at converting information between different formats, enabling them to translate the generated plans into representations interpretable by various critics.
  • Problem Specification Assistance: LLMs can assist users in refining problem specifications by asking clarifying questions and suggesting improvements.
  • Model Acquisition: LLMs can aid in acquiring the domain models used by the model-based critics, extracting relevant information from text and engaging in dialogue with domain experts to refine the models.

Human Involvement in the Framework

While the LLM-Modulo Framework aims to automate much of the planning and reasoning process, human involvement remains crucial in certain aspects. Domain experts play a role in acquiring and refining the domain models used by the model-based critics, while end users are involved in refining the problem specifications through interaction with the LLM.
However, the framework seeks to minimize the need for human intervention in the time-consuming task of iterative plan critiquing. By automating the feedback loop between the LLM and the critics, the framework enables efficient and scalable plan generation and refinement.

Case studies demonstrating the framework's effectiveness

The researchers applied the LLM-Modulo Framework to several planning domains to showcase its effectiveness. In the Blocks World domain, a classic planning benchmark, the performance of the LLM improved to an impressive 82% within 15 feedback rounds from a model-based verifier. This highlights the framework's ability to guide the LLM towards generating high-quality plans through iterative refinement.
In a more complex travel planning task, the LLM-Modulo Framework achieved a remarkable 6 times better performance compared to baseline approaches. By leveraging the generative power of the LLM and the rigorous validation of the critics, the framework successfully generated coherent and executable travel plans that satisfied various constraints and preferences.

The potential and promise of the LLM-Modulo framework

The research paper by Kambhampati and his team sheds light on the capabilities and limitations of LLMs in planning and reasoning tasks. While LLMs cannot autonomously plan or self-verify, they can still play a productive role when combined with external verifiers in frameworks like LLM-Modulo.
The LLM-Modulo Framework harnesses the strengths of LLMs in generating candidate plans and ideas while ensuring the correctness and soundness of the plans through model-based critics. By automating the feedback loop and minimizing the need for human intervention, the framework enables efficient and scalable planning and reasoning.
The case studies demonstrate the potential of the LLM-Modulo Framework to extend the scope of planning to more flexible and expressive problem specifications. By combining the generative power of LLMs with the rigor of symbolic planning techniques, the framework offers a promising direction for tackling complex real-world planning and reasoning challenges.


As the field of artificial intelligence continues to evolve, the LLM-Modulo Framework serves as a testament to the importance of combining the strengths of different approaches to achieve robust and reliable planning and reasoning. By harnessing the power of LLMs and integrating them with symbolic planning techniques, researchers and practitioners can push the boundaries of what is possible in AI-driven problem-solving.


The acceptance of the research paper for a spotlight presentation at the prestigious International Conference on Machine Learning (ICML) 2024 further underscores the significance and impact of the LLM-Modulo Framework. The spotlight presentation and the accompanying tutorial session provide an excellent opportunity for the authors to share their insights and engage with the broader machine learning community.
As we look towards the future, the LLM-Modulo Framework offers a promising path for leveraging the power of language models in planning and reasoning tasks. By continuing to explore and refine this approach, researchers can unlock new frontiers in AI-driven problem-solving, paving the way for more intelligent and adaptive systems that can tackle complex real-world challenges.

Link to the paper: https://arxiv.org/abs/2402.01817

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