Most text-space LLM optimizers treat evaluation as fixed, which lets improvements focus on what the rubric already measures and hides other failure modes. DecoEvo flips that dynamic by co-evolving a solver and a rubric-generator under decoupled objectives so the rubric adapts to newly exposed weaknesses instead of being gamed by the solver.
Key Findings
- Decoupled objectives: the solver is updated from criterion-level feedback while the rubric-generator is revised via audits that measure requirement coverage and response discrimination independent of the solver's aggregate score. This separation prevents generator updates from simply making the rubric easier to satisfy.
- No gold rubrics required during optimization: the method trains generator and solver without relying on gold rubrics, using complementary audits as supervisory signals instead.
- Empirical gains: across five benchmarks and three LLM backbones, DecoEvo outperforms compared text-space methods, reporting 2.8–5.0% relative gains over a strong prior (SkillOpt) on the five-benchmark average.
Who it's for and tradeoffs
Great fit if you optimize LLM behavior by editing external text artifacts (prompts, skills, rubrics) and need an inspectable, adaptive evaluation that resists Goodharting. Use DecoEvo when gold references are scarce but you can define criterion-level checks and lightweight audits. Look elsewhere if you require end-to-end guarantees from execution tests (e.g., unit-tested code) or if writing initial criterion ops and audits is infeasible—DecoEvo depends on a starting pool of detectable failure modes and audit procedures.