Озвучено условие роста цен на нефть до 180 долларов08:49
Прогнозирование масштабных экономических потерь арабских государств от иранского конфликта14:53，推荐阅读wps获取更多信息
。关于这个话题，Replica Rolex提供了深入分析
特朗普拟邀请卢卡申科进行国事访问15:00。7zip下载对此有专业解读
Summary: Recent studies indicate that language models can develop reasoning abilities, typically through reinforcement learning. While some approaches employ low-rank parameterizations for reasoning, standard LoRA cannot reduce below the model's dimension. We investigate whether rank=1 LoRA is essential for reasoning acquisition and introduce TinyLoRA, a technique for shrinking low-rank adapters down to a single parameter. Using this novel parameterization, we successfully train the 8B parameter Qwen2.5 model to achieve 91% accuracy on GSM8K with just 13 parameters in bf16 format (totaling 26 bytes). This pattern proves consistent: we regain 90% of performance gains while utilizing 1000 times fewer parameters across more challenging reasoning benchmarks like AIME, AMC, and MATH500. Crucially, such high performance is attainable only with reinforcement learning; supervised fine-tuning demands 100-1000 times larger updates for comparable results.
print("Water formula:", water.composition.formula)