洞察 - Algorithms and Data Structures - # Transformer-based Planning

Improving Planning Capabilities of Transformers via Search Dynamics Bootstrapping

Q: How can the search dynamics bootstrapping method be extended to other types of reasoning tasks beyond planning, such as mathematical reasoning or commonsense reasoning?

The search dynamics bootstrapping method can be extended to other types of reasoning tasks by adapting the training data and model architecture to suit the specific requirements of the new tasks. For mathematical reasoning tasks, the training data could consist of sequences representing mathematical problems and their step-by-step solutions. The model could be trained to predict the search dynamics involved in solving these problems, such as the selection of operations or variables at each step. By incorporating execution traces of mathematical reasoning processes, the model can learn to generate optimal solutions efficiently. Similarly, for commonsense reasoning tasks, the training data could include scenarios that require reasoning about everyday situations or logical deductions. The model could be trained to predict the sequence of reasoning steps needed to arrive at a logical conclusion. By training on execution traces of commonsense reasoning processes, the model can learn to navigate through complex scenarios and generate appropriate responses. To extend the method to different types of reasoning tasks, it is essential to carefully design the training data to capture the specific reasoning processes involved. Additionally, the model architecture may need to be adapted to handle the unique characteristics of each task, such as incorporating domain-specific knowledge or constraints. By tailoring the approach to different types of reasoning tasks, the search dynamics bootstrapping method can be effectively applied to a wide range of problem-solving scenarios.

Q: How can the limitations of the current approach be improved to scale to even more complex planning problems?

One limitation of the current approach is the computational complexity associated with training on long token sequences, especially for more complex planning problems. To improve scalability to even more complex planning problems, several strategies can be employed: Curriculum Learning: Start with simpler tasks and gradually increase the complexity of the training data to allow the model to learn progressively more challenging planning strategies. Hierarchical Planning: Integrate hierarchical planning methods and temporal abstractions into the model to enable it to abstract over multiple time steps and states, reducing the search space for complex planning tasks. Efficient Heuristics: Incorporate better heuristics or value functions into the planning algorithm, similar to Monte Carlo Tree Search (MCTS), to limit the depth of exploration and improve efficiency in finding optimal solutions. Model Adaptation: Fine-tune the model on a diverse set of planning tasks to improve its generalization and adaptability to different problem domains. By implementing these strategies, the current approach can be enhanced to handle more complex planning problems efficiently and effectively.

Q: Could the insights from this work be used to enhance the reasoning capabilities of large language models in real-world applications?

The insights from this work can indeed be leveraged to enhance the reasoning capabilities of large language models in real-world applications. By training Transformers to predict search dynamics and generate optimal plans for complex planning tasks, we can improve their ability to perform multi-step reasoning and decision-making. In real-world applications, large language models can benefit from this approach by incorporating execution traces of reasoning processes into their training data. This can enable them to generate more accurate and efficient solutions for a wide range of tasks that require complex reasoning, such as problem-solving, decision-making, and logical deduction. By enhancing the reasoning capabilities of large language models, we can improve their performance in various applications, including natural language understanding, dialogue systems, and automated decision-making processes. The insights gained from this work can pave the way for more advanced and intelligent AI systems that can effectively reason and solve complex problems in real-world scenarios.

核心概念

Transformer models can be trained to imitate the search dynamics of the A* algorithm and then fine-tuned to discover more efficient search strategies for solving complex planning tasks.

摘要

The authors demonstrate how to train Transformer models to solve complex planning tasks by imitating the search dynamics of the A* algorithm. They generate synthetic datasets that capture the step-by-step execution trace of A* search, in addition to the optimal plan.

The key insights are:

Training Transformer models on the search execution traces, in addition to the optimal plans, significantly boosts their performance, especially in the low data regime.
The search-augmented Transformer models outperform larger solution-only models that directly predict the optimal plan, highlighting the importance of capturing the reasoning process.
The authors further improve the search-augmented models through a "search dynamics bootstrapping" method, where the model is fine-tuned to discover more efficient search strategies that require fewer steps to find the optimal plan.
Experiments on maze navigation and Sokoban puzzle tasks show that the final Searchformer model can solve 93.7% of test tasks while using 26.8% fewer search steps compared to the original A* implementation.

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统计

The Searchformer model generates execution traces that are on average 26.8% shorter than the traces generated by the A* algorithm.
The Searchformer model solves 93.7% of the test Sokoban tasks.

引用

"Transformer-based architectures (Vaswani et al., 2017) have demonstrated impressive performance in different tasks, including holding conversations at the human level (Shuster et al., 2022; OpenAI, 2022, 2023; Touvron et al., 2023), high-quality image understanding (Caron et al., 2021; Oquab et al., 2024; Assran et al., 2023) and video generation (Singer et al., 2023), multi-modal generation (Girdhar et al., 2023; Radford et al., 2021), and code completion (Roziere et al., 2023; OpenAI, 2021)."
"Despite these successes, Transformer-based architectures and LLMs still struggle when it comes to solving planning and reasoning tasks. Previous studies demonstrate that LLMs fall short in multi-step planning tasks (Valmeekam et al., 2023a,b) or when performing higher-order reasoning (Momennejad et al., 2023; Fan et al., 2020)."

从中提取的关键见解

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

by Lucas Lehner... 在 arxiv.org 04-30-2024

https://arxiv.org/pdf/2402.14083.pdf

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

更深入的查询

How can the search dynamics bootstrapping method be extended to other types of reasoning tasks beyond planning, such as mathematical reasoning or commonsense reasoning?

The search dynamics bootstrapping method can be extended to other types of reasoning tasks by adapting the training data and model architecture to suit the specific requirements of the new tasks. For mathematical reasoning tasks, the training data could consist of sequences representing mathematical problems and their step-by-step solutions. The model could be trained to predict the search dynamics involved in solving these problems, such as the selection of operations or variables at each step. By incorporating execution traces of mathematical reasoning processes, the model can learn to generate optimal solutions efficiently.
Similarly, for commonsense reasoning tasks, the training data could include scenarios that require reasoning about everyday situations or logical deductions. The model could be trained to predict the sequence of reasoning steps needed to arrive at a logical conclusion. By training on execution traces of commonsense reasoning processes, the model can learn to navigate through complex scenarios and generate appropriate responses.
To extend the method to different types of reasoning tasks, it is essential to carefully design the training data to capture the specific reasoning processes involved. Additionally, the model architecture may need to be adapted to handle the unique characteristics of each task, such as incorporating domain-specific knowledge or constraints. By tailoring the approach to different types of reasoning tasks, the search dynamics bootstrapping method can be effectively applied to a wide range of problem-solving scenarios.

How can the limitations of the current approach be improved to scale to even more complex planning problems?

One limitation of the current approach is the computational complexity associated with training on long token sequences, especially for more complex planning problems. To improve scalability to even more complex planning problems, several strategies can be employed:

Curriculum Learning: Start with simpler tasks and gradually increase the complexity of the training data to allow the model to learn progressively more challenging planning strategies.

Hierarchical Planning: Integrate hierarchical planning methods and temporal abstractions into the model to enable it to abstract over multiple time steps and states, reducing the search space for complex planning tasks.

Efficient Heuristics: Incorporate better heuristics or value functions into the planning algorithm, similar to Monte Carlo Tree Search (MCTS), to limit the depth of exploration and improve efficiency in finding optimal solutions.

Model Adaptation: Fine-tune the model on a diverse set of planning tasks to improve its generalization and adaptability to different problem domains.

By implementing these strategies, the current approach can be enhanced to handle more complex planning problems efficiently and effectively.

Could the insights from this work be used to enhance the reasoning capabilities of large language models in real-world applications?

The insights from this work can indeed be leveraged to enhance the reasoning capabilities of large language models in real-world applications. By training Transformers to predict search dynamics and generate optimal plans for complex planning tasks, we can improve their ability to perform multi-step reasoning and decision-making.
In real-world applications, large language models can benefit from this approach by incorporating execution traces of reasoning processes into their training data. This can enable them to generate more accurate and efficient solutions for a wide range of tasks that require complex reasoning, such as problem-solving, decision-making, and logical deduction.
By enhancing the reasoning capabilities of large language models, we can improve their performance in various applications, including natural language understanding, dialogue systems, and automated decision-making processes. The insights gained from this work can pave the way for more advanced and intelligent AI systems that can effectively reason and solve complex problems in real-world scenarios.