\n ## Line Chart: Performance Comparison of Math Models ### Overview This line chart compares the performance of three different model configurations – Baselines (Math-Instruct), Ours (Base), and Ours (Math Base) – across four different model sizes: DeepSeek-7B, Qwen2.5-1.5B, Qwen2.5-7B, and Qwen2.5-72B. The performance metric appears to be a score, ranging from approximately 45 to 75. ### Components/Axes * **X-axis:** Model Name (DeepSeek-7B, Qwen2.5-1.5B, Qwen2.5-7B, Qwen2.5-72B) * **Y-axis:** Performance Score (Scale from 45 to 75, with increments of 5) * **Legend:** * Blue Diamonds: Baselines (Math-Instruct) * Red Circles: Ours (Base) * Green Triangles: Ours (Math Base) ### Detailed Analysis **Baselines (Math-Instruct) - Blue Diamonds:** The line slopes upward consistently. * DeepSeek-7B: 46.59 * Qwen2.5-1.5B: 56.97 * Qwen2.5-7B: 63.29 * Qwen2.5-72B: 68.16 **Ours (Base) - Red Circles:** The line initially increases, then plateaus. * DeepSeek-7B: 50.29 * Qwen2.5-1.5B: 51.82 * Qwen2.5-7B: 64.19 * Qwen2.5-72B: 71.13 **Ours (Math Base) - Green Triangles:** The line slopes upward consistently and is generally the highest performing. * DeepSeek-7B: 55.35 * Qwen2.5-1.5B: 59.99 * Qwen2.5-7B: 67.17 * Qwen2.5-72B: 71.84 ### Key Observations * The "Ours (Math Base)" model consistently outperforms both "Baselines (Math-Instruct)" and "Ours (Base)" across all model sizes. * The performance of all models generally increases with model size. * The "Ours (Base)" model shows a relatively flat performance curve between DeepSeek-7B and Qwen2.5-1.5B, then a significant jump to Qwen2.5-7B. * The gap between "Ours (Math Base)" and the other two models widens as the model size increases. ### Interpretation The data suggests that incorporating a "Math Base" into the model architecture significantly improves performance on the evaluated task. The consistent upward trend for all models indicates that increasing model size generally leads to better results, but the "Math Base" provides a substantial boost. The plateau observed in "Ours (Base)" between the first two model sizes suggests that simply increasing model size isn't sufficient; architectural improvements (like the "Math Base") are crucial for realizing further gains. The widening gap between the models as size increases indicates that the benefits of the "Math Base" become more pronounced with larger models. This could be due to the "Math Base" enabling the model to better leverage the increased capacity of larger models.