Scaling Scientific Discovery Environments for Turn-Level Agentic RL

Abstract:Large language model agents have shown promising capabilities in data-driven scientific discovery tasks, where an agent interacts with an execution environment and produces a statistical claim. Long-horizon scientific analysis remains constrained by the lack of process supervised environments over real-world scientific data. This paper introduces SciDisco, a scalable framework for training Scientific Discovery agents in process-verifiable environments. SciThèque compiles hypotheses, datasets, hidden evidence graphs, and verifiers into task environments where analytical progress can be checked during interaction. DAG-grounded trajectory synthesis uses these environments to construct verifier-filtered multi-turn demonstrations. DiscoPO then uses the environment as the source of training signal, assigning turn-level credit to actions that produce verifiable analytical evidence. Experiments show that SciDisco-14B reaches state-of-the-art on hypothesis-driven scientific data analysis benchmarks.

Artificial Intelligence (cs.AI)

arXiv:2607.28990 [cs.AI]

(or arXiv:2607.28990v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.28990 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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