Solution Augmentation for ARC Problems Using GFlowNet: A Probabilistic Exploration Approach

TMLR Paper4808 Authors

Abstract: One of the core challenges in building general reasoning systems lies in generating diverse, human-aligned solution trajectories—different yet valid paths by which a problem can be solved. Prior approaches often rely on handcrafted templates, rule-based augmentations, or human demonstrations, which are limited in scalability and stylistic diversity. To address this, we explore the use of Generative Flow Networks (GFlowNets) for automated solution augmentation in reasoning tasks. We propose a framework that learns to generate diverse reasoning trajectories with probabilities proportional to their quality, guided by a human-inspired reward function and a novel geometric forward policy. This enables the generation of multiple plausible solution paths without relying on manual supervision. We evaluate our framework on the Abstraction and Reasoning Corpus (ARC-AGI), a benchmark designed to test compositional and abstract reasoning. Our results show that GFlowNets can effectively explore the space of valid reasoning processes, producing trajectories that are diverse, concise, and consistent with human reasoning patterns. These findings suggest that GFlowNets offer a promising foundation for modeling structured reasoning in automated trajectory generation. Our code is here: https://anonymous.4open.science/r/GFN_to_ARC-B500/

Submission Length: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Sungsoo_Ahn1

Submission Number: 4808