Self-Improvement for Fast, High-Quality Plan Generation
📰 ArXiv cs.AI
Learn how to generate high-quality plans using self-improvement techniques and decoder-only transformers for fast and efficient planning
Action Steps
- Train a decoder-only transformer on synthetic plan data to generate high-quality plans
- Use optimal data to fine-tune the model for better performance
- Apply self-improvement techniques to the model to generate plans in sub-exponential time
- Evaluate the quality of generated plans using relevant metrics
- Compare the performance of the self-improvement approach with other planning algorithms
Who Needs to Know This
Researchers and developers working on generative models and planning algorithms can benefit from this technique to improve the quality of generated plans
Key Insight
💡 Decoder-only transformers can generate high-quality plans for unseen problem instances when trained on optimal data
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🤖 Generate high-quality plans fast with self-improvement techniques and decoder-only transformers! 🚀
Key Takeaways
Learn how to generate high-quality plans using self-improvement techniques and decoder-only transformers for fast and efficient planning
Full Article
Title: Self-Improvement for Fast, High-Quality Plan Generation
Abstract:
arXiv:2605.03625v1 Announce Type: new Abstract: Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality solution. We address the challenge of producing high-quality plans, a computationally hard problem, in sub-exponential time. First, we demonstrate that, given optimal data, a decoder-only transformer can generate high-quality plans for unseen problem instances. Second, we sh
Abstract:
arXiv:2605.03625v1 Announce Type: new Abstract: Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality solution. We address the challenge of producing high-quality plans, a computationally hard problem, in sub-exponential time. First, we demonstrate that, given optimal data, a decoder-only transformer can generate high-quality plans for unseen problem instances. Second, we sh
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