GFlowNets cut radio ray tracing costs dramatically

This is a strong example of ML being used as a practical accelerator for classical simulation, not just as a benchmark model.

• –The key win is reframing exponential path enumeration as guided sampling, which directly targets the main runtime bottleneck in telecom ray tracing.
• –Physics-based action masking plus replay for rare valid paths shows careful domain adaptation rather than off-the-shelf RL.
• –Open-sourced JAX implementation and tutorial make the work reproducible and easier for other wireless researchers to build on.
• –If results transfer beyond synthetic setups, this could materially improve iteration speed for 5G/6G planning workflows.