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Mixed SFT outperforms next-chunk reasoning RL while consuming over 60 times less compute, reshaping our understanding of effective training strategies with no-CoT data.
Achieving similar performance to larger models with significantly less data and faster inference speeds could redefine efficiency benchmarks in foundation models.
AdvancedMathBench reveals that even state-of-the-art models struggle with advanced mathematical reasoning, achieving only 75.8% accuracy in proof generation.
Visual Pretraining outperforms text-only methods, revealing that rich visual cues can enhance language model performance in ways previously underestimated.
