The paper introduces ScienceIDE, a framework that transforms scientific code repositories into programmable environments for training scientific agents. These environments enable task generation, execution, and verification, supporting supervised fine-tuning and reinforcement learning. The approach demonstrates improvements in scientific code repair and general-purpose benchmarks.
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ScienceIDE provides a structured way to convert scientific code into environments for training agents, potentially improving their ability to handle scientific tasks and general programming challenges.
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