Submission and Evaluation

Submission Requirements

Please submit the following three items:

  1. Neural Network Components
    Include your model definition, training script, inference script, and any trained weights or checkpoints.

  2. Code Packaging
    Provide all source files, a requirements.txt listing dependencies, and a brief README.md with setup and run instructions.

  3. Solution Format
    Supply a single text file (result.txt) that encodes each node’s assignment to one of the two solution sets.

Evaluation

Submissions are assessed in two categories:

1. Distribution-wise Reinforcement Learning Methods

  • Training Time: total time spent training across instances
  • Inference Time: average time to produce a solution for a single test graph
  • Objective Value: primary score (e.g. MaxCut value, tour length, etc.)

2. Conventional Methods

  • Running Time: time to solve each test instance (no training phase)
  • Objective Value: final score achieved on the problem
Method Type Time Metric Optimization Metric
Distribution-wise RL Training + Inference Objective Value
Conventional (non-RL) Running Time only Objective Value

Notes:

  • Objective Value is defined by the problem (higher is better unless stated otherwise).
  • Inference Time is measured on GPU (when applicable) and averaged over all test cases.
  • All methods run on the same standardized server under fixed resource limits.
  • Final rankings may combine time and objective metrics with track-specific weights.