Author here. We used ADRS to automatically evolve a scheduling algorithm starting from a simple greedy policy. Surprisingly, it outperformed Uniform Progress, the NSDI'24 best paper algorithm by discovering "selective waiting" strategies and adaptive pattern recognition.
Counter-intuitive finding: starting from a weaker baseline (greedy) worked better than starting from the SOTA algorithm. The AI found new insights rather than incremental improvements.
The entire process took 5 hours and cost <$20. We're open-sourcing everything and have applied this to other systems problems (MoE load balancing with 5x speedup, see https://news.ycombinator.com/item?id=45688236).
Happy to answer questions about the methodology or results!
Author here. We used ADRS to automatically evolve a scheduling algorithm starting from a simple greedy policy. Surprisingly, it outperformed Uniform Progress, the NSDI'24 best paper algorithm by discovering "selective waiting" strategies and adaptive pattern recognition.
Counter-intuitive finding: starting from a weaker baseline (greedy) worked better than starting from the SOTA algorithm. The AI found new insights rather than incremental improvements.
The entire process took 5 hours and cost <$20. We're open-sourcing everything and have applied this to other systems problems (MoE load balancing with 5x speedup, see https://news.ycombinator.com/item?id=45688236).
Happy to answer questions about the methodology or results!
Awesome results! Did you use the simulator from the NSDI paper directly?