On the Ability of Transformers to Verify Plans
arXiv:2603.19954v1 Announce Type: new Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been limited. We take important steps towards addressing this gap by analyzing the ability of decoder-only models to verify whether a given plan correctly solves a given planning instance. To analyse the general setting where the number of objects -- and thus the effective input alphabet -- grows at test time, we introduce C*-RASP, an extension of C-RASP designed to establish length generalization guarantees for transformers under the simultaneous growth in sequence length and vocabulary size. Our results identify a large class of classical planning domains for which transformers can provably learn to verify long plans, and structural properties that significantly affects the learnability of length generalizable solutions. Empirical experiments corroborate our theory.
arXiv:2603.19954v1 Announce Type: new Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been limited. We take important steps towards addressing this gap by analyzing the ability of decoder-only models to verify whether a given plan correctly solves a given planning instance. To analyse the general setting where the number of objects -- and thus the effective input alphabet -- grows at test time, we introduce C*-RASP, an extension of C-RASP designed to establish length generalization guarantees for transformers under the simultaneous growth in sequence length and vocabulary size. Our results identify a large class of classical planning domains for which transformers can provably learn to verify long plans, and structural properties that significantly affects the learnability of length generalizable solutions. Empirical experiments corroborate our theory.
Executive Summary
This article makes significant strides in addressing the inconsistent performance of Transformers in AI planning tasks. By introducing C*-RASP, an extension of C-RASP, the authors analyze the ability of decoder-only models to verify plans in a setting where the input alphabet grows at test time. The results indicate that Transformers can provably learn to verify long plans in a large class of classical planning domains, with structural properties affecting learnability. Empirical experiments support the theory. This research has far-reaching implications for the applications and development of Transformers in AI planning.
Key Points
- ▸ Transformers show inconsistent success in AI planning tasks
- ▸ C*-RASP is introduced to analyze the ability of decoder-only models to verify plans
- ▸ Transformers can provably learn to verify long plans in certain classical planning domains
- ▸ Structural properties significantly affect the learnability of length generalizable solutions
Merits
Strength of Theoretical Foundations
The article provides a rigorous theoretical framework, establishing length generalization guarantees for Transformers under growing input alphabets.
Empirical Validation
The authors corroborate their theory with empirical experiments, providing tangible evidence for the effectiveness of C*-RASP.
Practical Implications
The results have significant implications for the development and application of Transformers in AI planning, enabling more robust and efficient verification of plans.
Demerits
Limitation to Classical Planning Domains
The article's findings are specifically tailored to classical planning domains, and it remains to be seen how C*-RASP performs in more complex or dynamic planning scenarios.
Assumption of Growing Input Alphabets
The analysis relies on the assumption of growing input alphabets at test time, which may not be representative of all real-world planning scenarios.
Expert Commentary
The article's contribution to the theoretical understanding of Transformers in AI planning is significant, and the introduction of C*-RASP provides a valuable framework for analyzing the ability of decoder-only models to verify plans. However, the limitations of the article, such as its focus on classical planning domains and assumption of growing input alphabets, should be acknowledged and addressed in future research. The implications of the results are far-reaching and have significant practical and policy implications for the development and application of AI planning systems.
Recommendations
- ✓ Future research should focus on extending the analysis to more complex or dynamic planning scenarios, as well as exploring the applicability of C*-RASP to other AI planning tasks.
- ✓ Developers and policymakers should prioritize the explainability and transparency of AI planning decisions, particularly in high-stakes applications, to ensure the trustworthiness and accountability of AI planning systems.
Sources
Original: arXiv - cs.AI
