Adaptive Multi-Task Learning
EvoNashMTL
EvoNashMTL is a research prototype that asks a narrow question with a concrete mechanism: can Nash bargaining-based multi-task weighting be improved by a slower evolutionary scheduler that adapts task influence over time?
The current repository is strongest as a method and stability project. The toy harness shows that the outer scheduler can move, can alter final task weights relative to plain Nash-MTL, and can avoid the old one-hot collapse mode after repair.
Core Idea
Two levels of task weighting
The method keeps Nash-MTL as the inner bargaining rule at each training step, then adds a slower replicator-style scheduler that updates task shares over time. Final weights are formed by modulating Nash weights with those scheduler shares.
Current Evidence
Mechanism first, benchmarks later
The clearest current evidence is toy-level: the scheduler is active, the final optimization signal changes relative to plain Nash-MTL, and the hard stability case can be stabilized. Real benchmark superiority is still unconfirmed.
What This Site Covers
About
The project framing, research question, and current scope.
Results
All five toy figures, plus the strongest takeaways from the notes in imgs/.
Method
The two-level weighting design, tuned toy setup, and why the scheduler modulates Nash instead of replacing it.
Repo
Where the code lives, how to run the prototype, and what to inspect during experiments.