metadata
license: apache-2.0
task_categories:
- text-generation
tags:
- benchmark
- agentic
- self-improvement
AutoDataBench
AutoDataBench is a benchmark to evaluate an agent’s ability to autonomously write new tasks that feed the self-improvement loop. Given an original benchmark task and a record of the target model attempting it, the agent must produce a new task for the same suite that meets practical acceptance criteria on validity, novelty, difficulty, and behavioural coverage.
- Paper: AutoDataBench: Can Agents Write the Data That Feeds the Self-Improvement Loop?
- Project page: https://autodatabench.com/
- Code: https://github.com/StarDewXXX/AutoDataBench
The dataset contains rollouts and rubrics for three executable agent benchmarks: automationbench, terminal-bench, and tb-science. It is designed to measure whether individual produced tasks meet the standards of a production data pipeline.
For usage and detailed scoring methodology, please refer to the GitHub repository.