## Line Graphs: Model Accuracy Comparison Across Datasets ### Overview The image contains three line graphs comparing the accuracy of two models, **MLA@1.4T** (green dashed line) and **Kimi Linear@1.4T** (blue solid line), across different datasets. Each graph tracks accuracy progression during training or evaluation phases. --- ### Components/Axes 1. **Graph (a)** - **X-axis**: "Train" (intervals: 0, 20, 40, 60, 80, 100) - **Y-axis**: "Accuracy" (range: 20–65) - **Legend**: Top-left corner, labels: - Green dashed line: MLA@1.4T - Blue solid line: Kimi Linear@1.4T 2. **Graph (b)** - **X-axis**: "MATH 500 Test" (intervals: 0, 20, 40, 60, 80, 100) - **Y-axis**: "Accuracy" (range: 70–94) - **Legend**: Top-left corner, same labels as Graph (a). 3. **Graph (c)** - **X-axis**: "AIME 2025" (intervals: 0, 20, 40, 60, 80, 100) - **Y-axis**: "Accuracy" (range: 10–25) - **Legend**: Top-left corner, same labels as Graph (a). --- ### Detailed Analysis #### Graph (a): Training Accuracy - **MLA@1.4T**: Starts at ~20% accuracy, steadily increases to ~50% by 100 steps. - **Kimi Linear@1.4T**: Begins at ~25%, surpasses MLA@1.4T after ~60 steps, reaching ~55% by 100 steps. - **Trend**: Both models improve, but Kimi Linear@1.4T outperforms MLA@1.4T in later stages. #### Graph (b): MATH 500 Test Accuracy - **MLA@1.4T**: Starts at ~75%, fluctuates between ~78%–86%, peaking at ~86%. - **Kimi Linear@1.4T**: Begins at ~78%, rises to ~88%, then dips slightly to ~86%. - **Trend**: Kimi Linear@1.4T maintains higher accuracy, with minor volatility. #### Graph (c): AIME 2025 Accuracy - **MLA@1.4T**: Starts at ~10%, rises to ~18%, then dips to ~16% before recovering to ~19%. - **Kimi Linear@1.4T**: Begins at ~12%, surges to ~24%, then declines to ~21% before rising to ~24%. - **Trend**: Kimi Linear@1.4T shows sharper initial gains but higher volatility. --- ### Key Observations 1. **Dataset-Specific Performance**: - Kimi Linear@1.4T excels in AIME 2025 (highest final accuracy: ~24%). - MLA@1.4T performs more consistently in MATH 500 Test. 2. **Training Dynamics**: - Kimi Linear@1.4T overtakes MLA@1.4T during training (Graph a) but shows instability in AIME 2025. 3. **Volatility**: - MLA@1.4T exhibits smoother trends in MATH 500 Test, while Kimi Linear@1.4T has sharper fluctuations. --- ### Interpretation The data suggests **task-dependent model efficacy**: - **Kimi Linear@1.4T** may be optimized for complex reasoning tasks (AIME 2025) but requires stabilization. - **MLA@1.4T** demonstrates robustness in standardized tests (MATH 500) but lags in advanced benchmarks. - Training dynamics indicate Kimi Linear@1.4T’s potential for rapid improvement but highlights trade-offs between speed and stability. No textual content in non-English languages is present. All values are approximate, with uncertainty due to visual estimation from the graph.