Nice paper on optimizing agents and their graders together.

The key finding is that you want to update your LLM grader alongside the agent instead of keeping it fixed, because the agent's failure modes change as it improves.

DUET alternates between the two.

Each round picks training tasks, runs the solver, has the grader score and critique the trajectories, then a tool-using updater revises either the grader or the solver while the other stays fixed.

On the Finance Agent Benchmark, the solver reaches 96.5%, above GEPA (94.4%) and SkillOpt (94.0%). It also beats both when they optimize against the benchmark's own reference evaluator.

Grader agreement rises from 68.4% to 78.5% on GDPval. Training the grader with the solver frozen moves agreement only from 67.9% to 68.8%.

Paper: https://academy.dair.ai/papers/duet-co-evolving-solver-and-grader-agents-2610.04087