exhibits unique characteristics that are distinct from the training on general data. For example, the mathematical and coding abilities of our model can keep improving over a longer period of training steps. Therefore filtering and proportion adjustments. We obtain code preference data based on compiler-feedback, and mathematical preference data based on the ground-truth labels. For reward model training, we initialize the Chinese tasks. Ultimately, DeepSeek-V2 Chat (RL) demonstrates further enhanced performance in both mathematical and coding tasks compared with DeepSeek-V2 Chat (SFT). These comparisons highlight the strengths

is trying to solve a mathematical problem Prompt Engineering February 2025 30 Yikes. That’s obviously the wrong answer. As a matter of fact, LLMs often struggle with mathematical tasks and can provide