arXiv:2505.13986v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances. However, effectively incorporating domain knowledge into RL frameworks for graph partitioning remains challenging, as existing approaches typically rely on unconstrained node-level actions that lead to large action spaces and inefficient exploration. In this paper, we propose RidgeCut, an RL framework that constrains the action space to enforce structure-aware partitioning in the Normal
The paper leverages recent advancements in reinforcement learning to address a long-standing challenge in graph partitioning, a fundamental problem in various computational fields.
This development could lead to more efficient and scalable solutions for complex optimization problems, impacting fields from logistics to AI model architecture.
The proposed RidgeCut framework introduces a novel action space constraint in RL for graph partitioning, potentially enabling more effective and structure-aware learning.
- · AI/ML researchers
- · Logistics and supply chain companies
- · Cloud computing providers
- · Hardware designers
- · Traditional heuristic-based optimization methods
Improved graph partitioning efficiency could accelerate the training and deployment of large-scale AI models.
More optimized graph structures might lead to breakthroughs in other combinatorial optimization problems, enhancing resource allocation.
The methodology could inspire similar structure-aware RL approaches in other domains, leading to a broader paradigm shift in automated problem-solving.
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Read at arXiv cs.AI