Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
- URL: http://arxiv.org/abs/2502.04402v1
- Date: Thu, 06 Feb 2025 08:07:35 GMT
- Title: Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
- Authors: Niccolò Grillo, Andrea Toccaceli, Joël Mathys, Benjamin Estermann, Stefania Fresca, Roger Wattenhofer,
- Abstract summary: This study focuses on the impact of the inductive bias of the architecture, different reward systems and the role of recurrent modeling in enabling sequential reasoning.
We show how these elements contribute to successful extrapolation on increasingly complex puzzles.
- Score: 18.982541044390384
- License:
- Abstract: Despite incredible progress, many neural architectures fail to properly generalize beyond their training distribution. As such, learning to reason in a correct and generalizable way is one of the current fundamental challenges in machine learning. In this respect, logic puzzles provide a great testbed, as we can fully understand and control the learning environment. Thus, they allow to evaluate performance on previously unseen, larger and more difficult puzzles that follow the same underlying rules. Since traditional approaches often struggle to represent such scalable logical structures, we propose to model these puzzles using a graph-based approach. Then, we investigate the key factors enabling the proposed models to learn generalizable solutions in a reinforcement learning setting. Our study focuses on the impact of the inductive bias of the architecture, different reward systems and the role of recurrent modeling in enabling sequential reasoning. Through extensive experiments, we demonstrate how these elements contribute to successful extrapolation on increasingly complex puzzles.These insights and frameworks offer a systematic way to design learning-based systems capable of generalizable reasoning beyond interpolation.
Related papers
- Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
Benchmarks are plagued by various biases, artifacts, or leakage.
Models may behave unreliably due to poorly explored failure modes.
causality offers an ideal framework to systematically address these challenges.
arXiv Detail & Related papers (2025-02-07T17:01:37Z) - Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond [61.18736646013446]
In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network.
Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena.
arXiv Detail & Related papers (2024-10-31T22:54:34Z) - Foundations and Frontiers of Graph Learning Theory [81.39078977407719]
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures.
Graph Neural Networks (GNNs), i.e. neural network architectures designed for learning graph representations, have become a popular paradigm.
This article provides a comprehensive summary of the theoretical foundations and breakthroughs concerning the approximation and learning behaviors intrinsic to prevalent graph learning models.
arXiv Detail & Related papers (2024-07-03T14:07:41Z) - Explainable data-driven modeling via mixture of experts: towards
effective blending of grey and black-box models [6.331947318187792]
We propose a comprehensive framework based on a "mixture of experts" rationale.
This approach enables the data-based fusion of diverse local models.
We penalize abrupt variations in the expert's combination to enhance interpretability.
arXiv Detail & Related papers (2024-01-30T15:53:07Z) - Improving Compositional Generalization Using Iterated Learning and
Simplicial Embeddings [19.667133565610087]
Compositional generalization is easy for humans but hard for deep neural networks.
We propose to improve this ability by using iterated learning on models with simplicial embeddings.
We show that this combination of changes improves compositional generalization over other approaches.
arXiv Detail & Related papers (2023-10-28T18:30:30Z) - A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
We propose a general bi-level probabilistic graphical reasoning framework called GBPGR.
In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models.
Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
arXiv Detail & Related papers (2023-09-16T09:15:37Z) - The Neural Race Reduction: Dynamics of Abstraction in Gated Networks [12.130628846129973]
We introduce the Gated Deep Linear Network framework that schematizes how pathways of information flow impact learning dynamics.
We derive an exact reduction and, for certain cases, exact solutions to the dynamics of learning.
Our work gives rise to general hypotheses relating neural architecture to learning and provides a mathematical approach towards understanding the design of more complex architectures.
arXiv Detail & Related papers (2022-07-21T12:01:03Z) - A neural anisotropic view of underspecification in deep learning [60.119023683371736]
We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation.
Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
arXiv Detail & Related papers (2021-04-29T14:31:09Z) - Understanding Deep Architectures with Reasoning Layer [60.90906477693774]
We show that properties of the algorithm layers, such as convergence, stability, and sensitivity, are intimately related to the approximation and generalization abilities of the end-to-end model.
Our theory can provide useful guidelines for designing deep architectures with reasoning layers.
arXiv Detail & Related papers (2020-06-24T00:26:35Z) - Relational Neural Machines [19.569025323453257]
This paper presents a novel framework allowing jointly train the parameters of the learners and of a First-Order Logic based reasoner.
A Neural Machine is able recover both classical learning results in case of pure sub-symbolic learning, and Markov Logic Networks.
Proper algorithmic solutions are devised to make learning and inference tractable in large-scale problems.
arXiv Detail & Related papers (2020-02-06T10:53:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.