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AI Research
arXiv CS.AI·Jun 10·5 min read
Baseline-Free Policy Optimization for Neural Combinatorial Optimization
TL;DR
Neural combinatorial optimization (NCO) faces challenges with standard training methods that rely on a baseline, which can destabilize learning. The authors introduce Group Relative Policy Optimization (GRPO), a baseline-free algorithm that normalizes advantages across sampled trajectories.
✦ Why It Matters
Engineers can adopt GRPO for more stable and effective training in neural combinatorial optimization tasks.
Key Takeaways
- Implement GRPO to avoid training collapse in NCO tasks.
- Achieve competitive solution quality without relying on external baselines.
- Test GRPO on TSP and CVRP for improved performance.
- Explore advantages of baseline-free methods in complex routing problems.
How It Works
GRPO operates by normalizing the advantages of sampled trajectories within groups, which stabilizes the training process and reduces variance in gradient estimates. This approach eliminates the need for a frozen baseline policy, addressing the structural vulnerabilities associated with traditional methods.