Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning

Chaojie Wang, Yanchen Deng, Zhiyi Lv, Shuicheng Yan, An Bo·June 20, 2024

Summary

The paper series presents Q*, a framework that enhances large language models (LLMs) for multi-step reasoning by incorporating deliberative planning. Q* addresses LLMs' limitations in consistency and accuracy by learning a plug-and-play Q-value model as a heuristic, guiding decision-making without extensive fine-tuning. It employs MDPs, A* search, and offline reinforcement learning to estimate optimal actions, outperforming existing methods in tasks like math reasoning and code generation. Experiments on GSM8K, MATH, and MBPP datasets show significant improvements in performance, making Q* a versatile and efficient tool for enhancing LLMs' System 2 reasoning abilities. The research highlights the potential of these models in complex problem-solving and the importance of combining different techniques for better decision-making.

Key findings

1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of estimating the optimal Q-value of state-action pairs with a frozen LLM policy, which may be suboptimal for reasoning problems. It proposes learning a proxy Q-value model to approximate Q* from a dataset, enhancing multi-step reasoning for LLMs with deliberative planning . This problem is not entirely new, as prior attempts have been made to enhance System 2 reasoning capability using different methods like tree search algorithms and Monte Carlo Tree Search . However, the paper introduces a novel framework, Q*, which offers a general, versatile, and agile approach to improving multi-step reasoning for LLMs .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that the Q* method, which combines PRM (Path Ranking Model) and QVM (Q-Value Model), achieves the best performance among methods based on the Llama-2-7b model in multi-step reasoning tasks, surpassing the performance of closed-source models like ChatGPT-turbo . The study aims to demonstrate the effectiveness of the Q* method in guiding large language models to solve various tasks without the need for fine-tuning, thereby improving performance in math reasoning and code generation tasks .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Improving Multi-step Reasoning for LLMs with Deliberative Planning" introduces several innovative ideas, methods, and models to enhance the performance of large language models (LLMs) in multi-step reasoning tasks . Here are some key proposals from the paper:

  1. Q Model*: The paper introduces the Q* model, which aims to align LLMs' output with ground truth without relying on massive human-generated corpora or modifying the parameters of LLMs . Q* achieves alignment with distinct advantages compared to existing methods like SFT, RLHF, and DPO, making it suitable for various reasoning tasks without performance loss on other tasks .

  2. Tree-of-Thoughts (ToT): The paper discusses the Tree-of-Thoughts approach, which enhances LLMs' reasoning capabilities by exploring intermediate steps in problem-solving using basic tree-search algorithms . Additionally, techniques like A* search and Monte Carlo Tree Search (MCTS) are employed as planning techniques to improve LLM performance in solving complex reasoning problems .

  3. Math Reasoning and Code Generation: The paper addresses the challenges of multi-step reasoning in math reasoning and code generation tasks for LLMs . It discusses different techniques such as prompt engineering, fine-tuning with math/code corpus, and training verifiers to rank candidate solutions without providing intermediate step guidance . These approaches aim to enhance LLMs' performance in handling relations, quantities, and logics in challenging tasks.

  4. Innovative Training Approaches: The paper highlights the importance of training large language models using innovative methods such as self-debugging and self-consistency to improve their performance . These training approaches aim to enhance the capabilities of LLMs in various tasks by teaching them to self-correct and improve their reasoning abilities.

Overall, the paper presents a comprehensive set of novel ideas, methods, and models to empower large language models with optimal planning proficiency and enhance their performance in multi-step reasoning tasks across different domains . The Q* method proposed in the paper "Improving Multi-step Reasoning for LLMs with Deliberative Planning" introduces several key characteristics and advantages compared to previous methods, as detailed in the paper :

  1. Distinct Merits in Alignment: Unlike existing methods like SFT and Aligner, Q* achieves alignment without relying on massive human-generated corpora, which can be costly to collect. Additionally, Q* does not modify the parameters of LLMs like RLHF and DPO, thereby avoiding potential performance loss on other tasks .

  2. Planning Efficiency with Tree-of-Thoughts (ToT): The Tree-of-Thoughts approach enhances LLMs' reasoning capabilities by exploring intermediate steps using basic tree-search algorithms. Techniques like A* search and Monte Carlo Tree Search (MCTS) are employed to improve LLM performance in solving complex reasoning problems. Q* stands out by relying solely on ground-truth to train the value model, making it easily applicable to various reasoning tasks without the need for extensive modifications .

  3. Math Reasoning and Code Generation: In tasks like math reasoning and code generation, Q* addresses challenges by leveraging techniques such as prompt engineering, fine-tuning with math/code corpus, and training verifiers to rank candidate solutions without providing intermediate step guidance. Q* outperforms the Best-of-N method in code generation tasks, achieving promising accuracy levels without the need for extensive fine-tuning .

  4. Optimal Policy and A Search*: Q* establishes an optimal policy based on the optimal Q-function, satisfying the Bellman optimality equation. It utilizes A* search, a heuristic search algorithm, to guide LLMs in selecting the most promising next step during multi-step reasoning tasks. A* search ensures efficient exploration of reasoning sequences without the need for costly fine-tuning of LLMs for each specific task beforehand .

Overall, the Q* method offers a versatile and agile deliberation framework for LLMs, providing distinct advantages in alignment, planning efficiency, math reasoning, code generation, and optimal policy selection compared to traditional methods, as detailed in the paper .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of improving multi-step reasoning for Large Language Models (LLMs) with deliberative planning. Noteworthy researchers in this field include Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor Bursztyn, Ryan A Rossi, Somdeb Sarkhel, Chao Zhang, Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, and many others .

The key to the solution mentioned in the paper involves the Q* method, which leverages plug-and-play Q-value models as heuristic functions to guide LLMs in solving various tasks without the need for fine-tuning beforehand. This method allows for effective task-solving without performance degradation on other tasks and considers only a single step at a time, leading to superior performance improvements compared to other methods .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of different methods based on Llama-2-7b and other base models in mathematical reasoning . The experiments involved comparing the effectiveness of the Q* method with other baselines on the MATH dataset and GSM8K dataset . Various models, such as Llama-2-7b fine-tuned on Synthetic Data and DeepSeek-Math-7b, were used as base models to assess the impact of the Q* method on improving performance . The experiments aimed to demonstrate that the Q* method could lead to significant performance improvements compared to existing methods, especially in mathematical reasoning tasks .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MATH dataset . The code for the base model DeepSeek-Math-7b, which is considered one of the most powerful open-source models for math reasoning, is open source .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study introduces the Q* method, a framework designed to enhance the multi-step reasoning capabilities of Large Language Models (LLMs) through deliberative planning . The results demonstrate that the Q* method outperforms other baselines, such as ChatGPT-turbo, on the GSM8K dataset, achieving an accuracy of 78.8% . This indicates that the Q* method combining PRM and QVM leads to superior performance compared to other methods based on the Llama-2-7b model .

Moreover, the study evaluates the effectiveness of the Q* method on the MATH dataset, showing significant improvements over other models. For instance, the Q* method based on DeepSeek-Math-7b achieves an accuracy of 50.8%, surpassing the performance of closed-source models like Gemini Ultra . The results suggest that the Q* method can enhance the performance of LLMs in mathematical reasoning tasks, supporting the hypothesis that deliberative planning can lead to better outcomes in complex reasoning problems .

Furthermore, the comparison of Q* with other baselines on the MBPP dataset also reinforces the scientific hypotheses put forward in the study. The Q* method, when combined with PRM and QVM, achieves an accuracy of 77.0%, outperforming models like GPT-4 and CodeQwen1.5-7b-Chat . This highlights the effectiveness of the Q* framework in guiding LLMs to solve various tasks without the need for extensive fine-tuning, supporting the hypothesis that Q* can effectively improve multi-step reasoning capabilities without performance degradation on other tasks .

In conclusion, the experiments and results presented in the paper provide robust evidence to support the scientific hypotheses underlying the development and implementation of the Q* method for enhancing the multi-step reasoning abilities of Large Language Models through deliberative planning. The superior performance of Q* across different datasets and tasks demonstrates its effectiveness in improving the reasoning capabilities of LLMs, validating the hypotheses put forward in the study .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Introducing the Q* method for improving multi-step reasoning in Large Language Models (LLMs) with deliberative planning .
  • Demonstrating that the Q* method leads to performance improvements compared to the Best-of-N method, particularly in math reasoning tasks .
  • Showing that the Q* method, when applied to the DeepSeek-Math-7b model, surpasses closed-source models on the MATH dataset leaderboard, such as Gemini Ultra (4-shot) .

What work can be continued in depth?

The work that can be continued in depth involves enhancing System 2 reasoning capability for solving complex reasoning problems. This includes performing deliberation with basic tree search algorithms like BFS or DFS, Monte Carlo Tree Search (MCTS), and A* . These methods aim to improve multi-step reasoning by guiding Large Language Models (LLMs) with deliberative planning, allowing them to select the most promising next step without the need for fine-tuning for each specific task . However, challenges exist in designing utility functions for these methods, which can be laborious and difficult to extend to new scenarios .

Tables

3

Introduction
Background
Evolution of large language models and their limitations in multi-step reasoning
Importance of deliberative planning in improving consistency and accuracy
Objective
To develop a framework that enhances LLMs with deliberative planning
Introduce Q* as a plug-and-play solution for better decision-making
Method
Data Collection
Selection of datasets: GSM8K, MATH, and MBPP for evaluation
Task variety: Math reasoning and code generation to showcase versatility
Data Preprocessing
Adaptation of datasets for MDP (Markov Decision Process) representation
Handling input and output formats for A* search and reinforcement learning
MDP Integration
Formulation of MDPs for the problem-solving tasks
State and action spaces definition
A* Search
Implementation of the A* algorithm for action planning
Heuristic function for guiding the search
Offline Reinforcement Learning
Training the Q-value model using offline RL techniques
Estimation of optimal actions and their rewards
Experiments and Evaluation
Performance comparison with existing methods
Quantitative analysis of improvements in consistency and accuracy
Qualitative analysis of generated solutions
Results and Discussion
Demonstrated improvements on GSM8K, MATH, and MBPP datasets
System 2 reasoning enhancement in LLMs
Implications for complex problem-solving and model efficiency
Future Directions
Potential extensions and applications of Q* to other domains
Combining Q* with other AI techniques for enhanced decision-making
Conclusion
Summary of key findings and contributions
The significance of Q* in enhancing large language models for multi-step reasoning
Implications for the future of AI research and development.
Basic info
papers
artificial intelligence
Advanced features
Insights
What is the primary focus of the paper series Q*?
What techniques does Q* employ to estimate optimal actions in MDPs?
How does Q* address the limitations of large language models in multi-step reasoning?
In which datasets does Q* demonstrate improved performance compared to existing methods?

Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning

Chaojie Wang, Yanchen Deng, Zhiyi Lv, Shuicheng Yan, An Bo·June 20, 2024

Summary

The paper series presents Q*, a framework that enhances large language models (LLMs) for multi-step reasoning by incorporating deliberative planning. Q* addresses LLMs' limitations in consistency and accuracy by learning a plug-and-play Q-value model as a heuristic, guiding decision-making without extensive fine-tuning. It employs MDPs, A* search, and offline reinforcement learning to estimate optimal actions, outperforming existing methods in tasks like math reasoning and code generation. Experiments on GSM8K, MATH, and MBPP datasets show significant improvements in performance, making Q* a versatile and efficient tool for enhancing LLMs' System 2 reasoning abilities. The research highlights the potential of these models in complex problem-solving and the importance of combining different techniques for better decision-making.
Mind map
Estimation of optimal actions and their rewards
Training the Q-value model using offline RL techniques
Heuristic function for guiding the search
Implementation of the A* algorithm for action planning
State and action spaces definition
Formulation of MDPs for the problem-solving tasks
Qualitative analysis of generated solutions
Quantitative analysis of improvements in consistency and accuracy
Performance comparison with existing methods
Offline Reinforcement Learning
A* Search
MDP Integration
Task variety: Math reasoning and code generation to showcase versatility
Selection of datasets: GSM8K, MATH, and MBPP for evaluation
Introduce Q* as a plug-and-play solution for better decision-making
To develop a framework that enhances LLMs with deliberative planning
Importance of deliberative planning in improving consistency and accuracy
Evolution of large language models and their limitations in multi-step reasoning
Implications for the future of AI research and development.
The significance of Q* in enhancing large language models for multi-step reasoning
Summary of key findings and contributions
Combining Q* with other AI techniques for enhanced decision-making
Potential extensions and applications of Q* to other domains
Implications for complex problem-solving and model efficiency
System 2 reasoning enhancement in LLMs
Demonstrated improvements on GSM8K, MATH, and MBPP datasets
Experiments and Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Directions
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Evolution of large language models and their limitations in multi-step reasoning
Importance of deliberative planning in improving consistency and accuracy
Objective
To develop a framework that enhances LLMs with deliberative planning
Introduce Q* as a plug-and-play solution for better decision-making
Method
Data Collection
Selection of datasets: GSM8K, MATH, and MBPP for evaluation
Task variety: Math reasoning and code generation to showcase versatility
Data Preprocessing
Adaptation of datasets for MDP (Markov Decision Process) representation
Handling input and output formats for A* search and reinforcement learning
MDP Integration
Formulation of MDPs for the problem-solving tasks
State and action spaces definition
A* Search
Implementation of the A* algorithm for action planning
Heuristic function for guiding the search
Offline Reinforcement Learning
Training the Q-value model using offline RL techniques
Estimation of optimal actions and their rewards
Experiments and Evaluation
Performance comparison with existing methods
Quantitative analysis of improvements in consistency and accuracy
Qualitative analysis of generated solutions
Results and Discussion
Demonstrated improvements on GSM8K, MATH, and MBPP datasets
System 2 reasoning enhancement in LLMs
Implications for complex problem-solving and model efficiency
Future Directions
Potential extensions and applications of Q* to other domains
Combining Q* with other AI techniques for enhanced decision-making
Conclusion
Summary of key findings and contributions
The significance of Q* in enhancing large language models for multi-step reasoning
Implications for the future of AI research and development.
Key findings
1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of estimating the optimal Q-value of state-action pairs with a frozen LLM policy, which may be suboptimal for reasoning problems. It proposes learning a proxy Q-value model to approximate Q* from a dataset, enhancing multi-step reasoning for LLMs with deliberative planning . This problem is not entirely new, as prior attempts have been made to enhance System 2 reasoning capability using different methods like tree search algorithms and Monte Carlo Tree Search . However, the paper introduces a novel framework, Q*, which offers a general, versatile, and agile approach to improving multi-step reasoning for LLMs .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate the scientific hypothesis that the Q* method, which combines PRM (Path Ranking Model) and QVM (Q-Value Model), achieves the best performance among methods based on the Llama-2-7b model in multi-step reasoning tasks, surpassing the performance of closed-source models like ChatGPT-turbo . The study aims to demonstrate the effectiveness of the Q* method in guiding large language models to solve various tasks without the need for fine-tuning, thereby improving performance in math reasoning and code generation tasks .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Improving Multi-step Reasoning for LLMs with Deliberative Planning" introduces several innovative ideas, methods, and models to enhance the performance of large language models (LLMs) in multi-step reasoning tasks . Here are some key proposals from the paper:

  1. Q Model*: The paper introduces the Q* model, which aims to align LLMs' output with ground truth without relying on massive human-generated corpora or modifying the parameters of LLMs . Q* achieves alignment with distinct advantages compared to existing methods like SFT, RLHF, and DPO, making it suitable for various reasoning tasks without performance loss on other tasks .

  2. Tree-of-Thoughts (ToT): The paper discusses the Tree-of-Thoughts approach, which enhances LLMs' reasoning capabilities by exploring intermediate steps in problem-solving using basic tree-search algorithms . Additionally, techniques like A* search and Monte Carlo Tree Search (MCTS) are employed as planning techniques to improve LLM performance in solving complex reasoning problems .

  3. Math Reasoning and Code Generation: The paper addresses the challenges of multi-step reasoning in math reasoning and code generation tasks for LLMs . It discusses different techniques such as prompt engineering, fine-tuning with math/code corpus, and training verifiers to rank candidate solutions without providing intermediate step guidance . These approaches aim to enhance LLMs' performance in handling relations, quantities, and logics in challenging tasks.

  4. Innovative Training Approaches: The paper highlights the importance of training large language models using innovative methods such as self-debugging and self-consistency to improve their performance . These training approaches aim to enhance the capabilities of LLMs in various tasks by teaching them to self-correct and improve their reasoning abilities.

Overall, the paper presents a comprehensive set of novel ideas, methods, and models to empower large language models with optimal planning proficiency and enhance their performance in multi-step reasoning tasks across different domains . The Q* method proposed in the paper "Improving Multi-step Reasoning for LLMs with Deliberative Planning" introduces several key characteristics and advantages compared to previous methods, as detailed in the paper :

  1. Distinct Merits in Alignment: Unlike existing methods like SFT and Aligner, Q* achieves alignment without relying on massive human-generated corpora, which can be costly to collect. Additionally, Q* does not modify the parameters of LLMs like RLHF and DPO, thereby avoiding potential performance loss on other tasks .

  2. Planning Efficiency with Tree-of-Thoughts (ToT): The Tree-of-Thoughts approach enhances LLMs' reasoning capabilities by exploring intermediate steps using basic tree-search algorithms. Techniques like A* search and Monte Carlo Tree Search (MCTS) are employed to improve LLM performance in solving complex reasoning problems. Q* stands out by relying solely on ground-truth to train the value model, making it easily applicable to various reasoning tasks without the need for extensive modifications .

  3. Math Reasoning and Code Generation: In tasks like math reasoning and code generation, Q* addresses challenges by leveraging techniques such as prompt engineering, fine-tuning with math/code corpus, and training verifiers to rank candidate solutions without providing intermediate step guidance. Q* outperforms the Best-of-N method in code generation tasks, achieving promising accuracy levels without the need for extensive fine-tuning .

  4. Optimal Policy and A Search*: Q* establishes an optimal policy based on the optimal Q-function, satisfying the Bellman optimality equation. It utilizes A* search, a heuristic search algorithm, to guide LLMs in selecting the most promising next step during multi-step reasoning tasks. A* search ensures efficient exploration of reasoning sequences without the need for costly fine-tuning of LLMs for each specific task beforehand .

Overall, the Q* method offers a versatile and agile deliberation framework for LLMs, providing distinct advantages in alignment, planning efficiency, math reasoning, code generation, and optimal policy selection compared to traditional methods, as detailed in the paper .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of improving multi-step reasoning for Large Language Models (LLMs) with deliberative planning. Noteworthy researchers in this field include Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor Bursztyn, Ryan A Rossi, Somdeb Sarkhel, Chao Zhang, Cameron B Browne, Edward Powley, Daniel Whitehouse, Simon M Lucas, Peter I Cowling, and many others .

The key to the solution mentioned in the paper involves the Q* method, which leverages plug-and-play Q-value models as heuristic functions to guide LLMs in solving various tasks without the need for fine-tuning beforehand. This method allows for effective task-solving without performance degradation on other tasks and considers only a single step at a time, leading to superior performance improvements compared to other methods .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of different methods based on Llama-2-7b and other base models in mathematical reasoning . The experiments involved comparing the effectiveness of the Q* method with other baselines on the MATH dataset and GSM8K dataset . Various models, such as Llama-2-7b fine-tuned on Synthetic Data and DeepSeek-Math-7b, were used as base models to assess the impact of the Q* method on improving performance . The experiments aimed to demonstrate that the Q* method could lead to significant performance improvements compared to existing methods, especially in mathematical reasoning tasks .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MATH dataset . The code for the base model DeepSeek-Math-7b, which is considered one of the most powerful open-source models for math reasoning, is open source .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study introduces the Q* method, a framework designed to enhance the multi-step reasoning capabilities of Large Language Models (LLMs) through deliberative planning . The results demonstrate that the Q* method outperforms other baselines, such as ChatGPT-turbo, on the GSM8K dataset, achieving an accuracy of 78.8% . This indicates that the Q* method combining PRM and QVM leads to superior performance compared to other methods based on the Llama-2-7b model .

Moreover, the study evaluates the effectiveness of the Q* method on the MATH dataset, showing significant improvements over other models. For instance, the Q* method based on DeepSeek-Math-7b achieves an accuracy of 50.8%, surpassing the performance of closed-source models like Gemini Ultra . The results suggest that the Q* method can enhance the performance of LLMs in mathematical reasoning tasks, supporting the hypothesis that deliberative planning can lead to better outcomes in complex reasoning problems .

Furthermore, the comparison of Q* with other baselines on the MBPP dataset also reinforces the scientific hypotheses put forward in the study. The Q* method, when combined with PRM and QVM, achieves an accuracy of 77.0%, outperforming models like GPT-4 and CodeQwen1.5-7b-Chat . This highlights the effectiveness of the Q* framework in guiding LLMs to solve various tasks without the need for extensive fine-tuning, supporting the hypothesis that Q* can effectively improve multi-step reasoning capabilities without performance degradation on other tasks .

In conclusion, the experiments and results presented in the paper provide robust evidence to support the scientific hypotheses underlying the development and implementation of the Q* method for enhancing the multi-step reasoning abilities of Large Language Models through deliberative planning. The superior performance of Q* across different datasets and tasks demonstrates its effectiveness in improving the reasoning capabilities of LLMs, validating the hypotheses put forward in the study .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Introducing the Q* method for improving multi-step reasoning in Large Language Models (LLMs) with deliberative planning .
  • Demonstrating that the Q* method leads to performance improvements compared to the Best-of-N method, particularly in math reasoning tasks .
  • Showing that the Q* method, when applied to the DeepSeek-Math-7b model, surpasses closed-source models on the MATH dataset leaderboard, such as Gemini Ultra (4-shot) .

What work can be continued in depth?

The work that can be continued in depth involves enhancing System 2 reasoning capability for solving complex reasoning problems. This includes performing deliberation with basic tree search algorithms like BFS or DFS, Monte Carlo Tree Search (MCTS), and A* . These methods aim to improve multi-step reasoning by guiding Large Language Models (LLMs) with deliberative planning, allowing them to select the most promising next step without the need for fine-tuning for each specific task . However, challenges exist in designing utility functions for these methods, which can be laborious and difficult to extend to new scenarios .

Tables
3
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