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Reinforcement learning has become a common post-training technique to improve the accuracy of LLMs on verifiable tasks. However, such training can narrow the set of solutions the model can produce. Several approaches have attempted to address this behavior-sharpening effect, but they often optimize metrics that do not...
This work presents AutoSDT, an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows and is the only automatically collected and the largest open dataset for data-driven scientific discovery.
D3-Gym is introduced, the first automatically constructed dataset with verifiable environments for scientific Data-Driven Discovery, and how D3-Gym environments can serve as a testbed for studying agentic optimization loops such as Autoresearch on real scientific workflows.
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