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

1. Planning involves envisioning steps to reach a goal.

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Planning requires mentally simulating actions to achieve objectives, crucial for solving complex problems efficiently.

• Planning is akin to mentally acting out scenarios before executing them.
• Envisioning different paths helps in avoiding obstacles and optimizing outcomes.
• Effective planning is essential in both virtual and real-world scenarios.

2. Planning involves creating a sequence of actions to achieve a goal.

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Plans are sequences of steps designed to navigate obstacles and reach desired outcomes efficiently.

• Plans can be represented as a series of tokens or language sequences.
• Effective planning requires considering contingencies and alternative paths.
• Planning involves foreseeing potential challenges and devising strategies to overcome them.

3. Language models can be trained to mimic planning algorithms.

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Transformers can be taught to replicate planning processes, potentially reducing search steps for optimal plans.

• Training language models to understand planning tasks can enhance problem-solving capabilities.
• The output of language models may not always guarantee optimal or valid plans due to inherent model limitations.
• Exploring the intersection of language models and planning is a key focus for future advancements.

4. Explicitly teaching language models planning improves accessibility.

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Teaching language models how to think about planning problems enhances their ability to tackle planning tasks effectively.

• Language models can be trained to understand planning processes for improved problem-solving.
• Enhancing language models with planning knowledge can make planning tasks more accessible to them.

5. Execution traces play a vital role in planning algorithms.

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Understanding and utilizing execution traces are crucial for planning algorithms to generate optimal plans efficiently.

• Execution traces guide the planning algorithm through steps to reach optimal solutions.
• Different planning algorithms may have varying execution trace complexities.

6. Heuristics like A* algorithm balance distance and goal proximity.

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A* algorithm combines distance from the start and heuristic distance to the goal to optimize pathfinding.

• Heuristics help in estimating distances and guide the algorithm towards the goal efficiently.
• Admissible heuristics ensure underestimation of distances for optimal planning.

7. Training language models with execution traces enhances planning efficiency.

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Training language models with execution traces improves planning efficiency and the generation of optimal or near-optimal plans.

• Language models can be trained to produce execution traces for better planning outcomes.
• Efficient planning involves teaching models to understand and generate optimal plans.

8. Teaching language models about planning improves performance.

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Instructing AI models on planning tasks enhances their capabilities, reducing the need for extensive training data and improving performance.

• Explicitly teaching AI models planning concepts enhances their efficiency.
• Models trained on planning tasks with guidance outperform those without explicit planning instruction.
• Reducing reliance on vast training data by teaching planning concepts leads to improved AI performance.

9. Augmenting models with search dynamics enhances solution quality.

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Implementing methods to alter how decoders generate execution traces improves model performance, leading to more optimal and varied solutions.

• Introducing non-deterministic elements in model training enhances solution diversity.
• Varying the search order while maintaining cost calculations boosts model effectiveness.
• Augmented models approximate training sequence probabilities, improving plan generation.

10. Training models with reduced length data set produces optimal plans.

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Utilizing shorter execution traces in training sets results in models generating optimal plans with shorter execution paths.

• Replacing longer training samples with shorter ones leads to more efficient plan generation.
• Shorter execution traces in training data set yield optimal plans by construction.
• Models trained on reduced length data consistently produce optimal plans.