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Scaling Scientific Discovery Environments for Turn-Level Agentic RL

Research LLM Agents

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TL;DR - SciDisco is a framework for training LLM-based scientific discovery agents inside process-verifiable environments, using turn-level RL credit assignment rather than only final-answer rewards. It matters because long-horizon data analysis has lacked environments that can check intermediate analytical progress, not just the final claim.

  • SciThèque compiles hypotheses, datasets, hidden evidence graphs, and verifiers into task environments where analytical progress is checkable mid-interaction.
  • DAG-grounded trajectory synthesis uses those environments to generate verifier-filtered multi-turn demonstrations for supervised bootstrapping.
  • DiscoPO treats the environment itself as the training signal, assigning turn-level credit to actions yielding verifiable analytical evidence.
  • The resulting SciDisco-14B model is reported as state-of-the-art on hypothesis-driven scientific data analysis benchmarks; no specific metrics or baselines are given in the abstract.

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Scaling Scientific Discovery Environments for Turn-Level Agentic RL

arXiv cs.AI Yucheng Xu, Keyi Zhang, Yuyang Yu, Min Zhang, Shiyuan Meng, Pei Chu, Zhongying Tu 2026-07-31 arXiv:2607.28990
Public signals Semantic Scholar citations 2 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 2 · Influential citations 0 X · N/A Fetched 2026-08-30 14:27:46.884511 UTC

TL;DR - SciDisco is a framework for training LLM-based scientific discovery agents inside process-verifiable environments, using turn-level RL credit assignment rather than only final-answer rewards. It matters because long-horizon data analysis has lacked environments that can check intermediate analytical progress, not just the final claim.

  • SciThèque compiles hypotheses, datasets, hidden evidence graphs, and verifiers into task environments where analytical progress is checkable mid-interaction.
  • DAG-grounded trajectory synthesis uses those environments to generate verifier-filtered multi-turn demonstrations for supervised bootstrapping.
  • DiscoPO treats the environment itself as the training signal, assigning turn-level credit to actions yielding verifiable analytical evidence.
  • The resulting SciDisco-14B model is reported as state-of-the-art on hypothesis-driven scientific data analysis benchmarks; no specific metrics or baselines are given in the abstract.
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