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verifiers offers built in support for GEPA, an algorithm that optimizes a system prompt to maximize the downstream reward for a given taskset:
gepa runs GEPA where a number of rollouts are done before a teacher LLM reflects on the results to propose a better Task.system_prompt without any gradient based training. It runs against native v1 tasksets. GEPA reuses the same taskset / harness / client / sampling config as eval, so the .toml config remains very similar:
Validate the config by using uv run gepa @ config.toml --dry-run. To run GEPA, use uv run gepa @ config.toml. CLI arguments overwrite toml arguments when both are present.

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Common config values

  • model / -m — model for the rollouts under optimization (default: deepseek/deepseek-v4-flash, same as eval)
  • reflection_model / reflection_client — model/endpoint that proposes new prompts (default: reuse model / client)
  • num_train / num_val — train tasks for reflection minibatches and held-out val tasks for the pareto frontier (defaults: 100 / 50)
  • max_total_rollouts — total rollouts the run may spend (default: 500)
  • max_concurrent / -c — caps how many rollouts are in flight at once (default: 128)

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Output

Results go under outputs/<taskset>--<model>--<harness>/<uuid>/, matching eval. The best system prompt is printed when the run finishes.

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Limitations

Tasksets — GEPA optimizes Task.system_prompt, so the taskset must provide one. Tasksets that bake instructions into the user prompt instead (e.g. gsm8k-v1) are not supported out of the box. Harnesses — any eval harness works. With APPENDS_SYSTEM_PROMPT, the optimized prompt is used as a system message but otherwise is folded into the user prompt.