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[2602.23148] On Sample-Efficient Generalized Planning via Learned Transition Models
Summary
This paper explores sample-efficient generalized planning through learned transition models, demonstrating improved performance over traditional action-sequence prediction methods.
Why It Matters
The research addresses the limitations of current generalized planning techniques that rely heavily on large datasets and model sizes. By proposing a method that learns explicit transition models, it offers a more efficient approach to planning in AI, which can lead to advancements in various applications, including robotics and automated decision-making.
Key Takeaways
- Introduces a novel approach to generalized planning via learned transition models.
- Demonstrates that explicit transition modeling enhances out-of-distribution plan success.
- Achieves better sample efficiency with smaller models compared to traditional methods.
- Evaluates multiple state representations and neural architectures for optimal performance.
- Provides insights into the future of planning in AI and its applications.
Computer Science > Artificial Intelligence arXiv:2602.23148 (cs) [Submitted on 26 Feb 2026] Title:On Sample-Efficient Generalized Planning via Learned Transition Models Authors:Nitin Gupta, Vishal Pallagani, John A. Aydin, Biplav Srivastava View a PDF of the paper titled On Sample-Efficient Generalized Planning via Learned Transition Models, by Nitin Gupta and 3 other authors View PDF HTML (experimental) Abstract:Generalized planning studies the construction of solution strategies that generalize across families of planning problems sharing a common domain model, formally defined by a transition function $\gamma : S \times A \rightarrow S$. Classical approaches achieve such generalization through symbolic abstractions and explicit reasoning over $\gamma$. In contrast, recent Transformer-based planners, such as PlanGPT and Plansformer, largely cast generalized planning as direct action-sequence prediction, bypassing explicit transition modeling. While effective on in-distribution instances, these approaches typically require large datasets and model sizes, and often suffer from state drift in long-horizon settings due to the absence of explicit world-state evolution. In this work, we formulate generalized planning as a transition-model learning problem, in which a neural model explicitly approximates the successor-state function $\hat{\gamma} \approx \gamma$ and generates plans by rolling out symbolic state trajectories. Instead of predicting actions directly, the model aut...
