{"data":{"kind":"file","path":"README.md","version_id":"tcjm2fsb23errggdb2rqzehw","entry":{"name":"README.md","path":"README.md","is_directory":false,"size":911,"modified_at":"2025-09-04T12:12:47.425000","content_hash":"3f6247a1b4211ce523ea6c94a85d07d84a02bcc1861d8fb73df82999055b4c75"},"entries":[],"content":"# hello-world-multi-turn\n\n### Overview\n- **Environment ID**: `hello-world-multi-turn`\n- **Short description**: Standalone example to demonstrate how multi-turn examples work.\n- **Tags**: hello-world, multi-turn\n\n### Datasets\n- **Primary dataset(s)**: N/A\n- **Source links**: https://github.com/stangirala/prime-envs\n- **Split sizes**: N/A\n\n### Task\n- **Type**: multi-turn\n- **Parser**: XMLParser\n- **Rubric overview**: Dummy reward, return 1.0 to keep the conversation moving.\n\n### Quickstart\nRun an evaluation with default settings:\n\nSetup OAI keys with `OPENAI_API_KEY` environment variable.\n\nFor the dummy dataset with one record, use the follow to test the code,\n\n```bash\nuv run vf-eval hello-world-multi-turn -m gpt-4.1-mini -n 1 --save-dataset\n\n```\n\n### Metrics\nSummarize key metrics your rubric emits and how they’re interpreted.\n\n| Metric | Meaning |\n| ------ | ------- |\n| `reward` | Dummy reward |\n\n","encoding":"utf-8","truncated":false,"total_bytes":911},"status":null}