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The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities.
A compact 1.7B parameter policy can outperform larger models in recommendation tasks by leveraging simulated user feedback for training.
LLMs may excel at predicting outcomes but often fail to grasp the underlying scientific validity of the laws they propose, revealing critical gaps in their discovery capabilities.
