# GEPA — faithful port of the reference implementation's defaults (github.com/gepa-ai/gepa).
# Sections mirror the six core components; only the components GEPA tunes appear here.
seed: 0                              # EngineConfig.seed default (the Pareto sampler's RNG)
general:
  max_iterations: 100
  # GEPA is not a diff method: the reflection LM returns a complete drop-in replacement, extracted
  # from the ``` block by GEPA's own outer-fence extractor (proposer.py). Naming it here keeps the
  # run's recorded config honest instead of inheriting the framework's diff default.
  mutation_approach: reflective_rewrite
  inner_retry_times: 1               # upstream proposes once per iteration; a failed proposal is
                                     # logged and the loop moves on (GEPAEngine.run exception path)
population:                          # GepaCandidatePool
  acceptance_criterion: strict_improvement   # StrictImprovementAcceptance (EngineConfig default);
                                             # "improvement_or_equal" allows lateral moves
selection_policy:                    # ParetoCandidateSelector
  frontier_type: hybrid              # EngineConfig.frontier_type default — instance keys UNION
                                     # objective keys. Also: instance | objective | cartesian.
proposer:                            # ReflectionConfig (gepa.lm.LM sends no sampling params, so the
  temperature: 1.0                   # provider default applies — 1.0 for the OpenAI-compatible APIs)
  retries: 3                         # LM(num_retries=3)
# NOT exposed, because each is structurally inert on galapagos's single-artifact candidate:
#   merge / max_merge_invocations   — system-aware merge needs two candidates that changed DIFFERENT
#                                     components of a common ancestor (upstream default: disabled).
#   module_selector                 — round-robin over one component.
#   reflection_minibatch_size       — single-instance mode pins it to 1 (upstream does the same).
#   max_metric_calls                — galapagos's only stop condition is general.max_iterations.
#   num_parallel_proposals / parallel — the galapagos loop is strictly sequential by design.