arXiv:2606.10321v1 Announce Type: new Abstract: Neural combinatorial optimization (NCO) trains autoregressive policies to solve routing problems. The standard training algorithm, REINFORCE with a rollout baseline, requires maintaining and periodically updating a frozen copy of the policy for variance reduction. This baseline introduces a structural vulnerability: on harder instances, a poor baseline produces noisy gradient estimates that can destabilize training. We evaluate Group Relative Policy Optimization (GRPO), an algorithm from large language model alignment that eliminates the baseline
The continuous push for more efficient and robust AI training methods, particularly for computationally intensive tasks like combinatorial optimization, drives innovation in this area.
Improved training stability and efficiency for neural combinatorial optimization could significantly enhance the performance and applicability of AI in complex logistics, robotics, and resource allocation problems.
The adoption of baseline-free optimization methods like GRPO promises more reliable and less volatile training for AI models tackling 'harder instances' of combinatorial problems, potentially accelerating development in these domains.
- · AI algorithm developers
- · Robotics
- · Logistics and supply chain management
- · Deep learning researchers
- · Inefficient AI training methods
- · Systems reliant on traditional 'rollout baseline' techniques
More effective AI solutions for routing and scheduling problems become deployable.
Reduced computational costs and faster convergence for training complex AI policies in real-world applications.
Enhanced automation and efficiency across industries currently constrained by the complexity of combinatorial optimization.
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Read at arXiv cs.LG