Beyond Correctness: Compute-Responsive Data Selection for Reasoning Model Post-Training
Reasoning language models increasingly rely on test-time compute scaling, yet most existing work studies how to allocate inference computation while treating the model itself as fixed. We ask a complementary question: can reasoning models be post-trained to make better use of a given amount of test-time compute, and wh...
Liang He, Xiang-Yu Li, Yuxuan Wang et al.
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