Mutant: Learning Congestion Control from Existing Protocols via Online Reinforcement Learning#
NSDI '25 digest Program Adjacent pick 28–30 April 2025
- Authors / tier: Lorenzo Pappone (Saint Louis University); Alessio Sacco (Politecnico di Torino); Flavio Esposito (Saint Louis University). Adjacent.
- Why: Congestion control is core datapath territory; the "learn to switch among existing CCAs online" framing is a pragmatic middle ground between hand-tuned and clean-slate learned CC, worth watching as a design pattern.
- What: An online-RL approach that adapts among existing congestion-control protocols rather than learning one from scratch. (Detailed mechanism and results not captured in this digest pass; confirm from the paper.)
- Where applicable: General transport; treat performance claims as to-be-verified.