## Line Graphs: Qwen2.5-7B vs. Llama3.1-8B Performance ### Overview The image presents two line graphs comparing the performance of two language models, Qwen2.5-7B and Llama3.1-8B, on Lichess puzzle accuracy over training steps. Each graph plots the "Win Rate" and "Normalized Rank" against the "Training Step." ### Components/Axes * **Titles:** * Left Graph: Qwen2.5-7B * Right Graph: Llama3.1-8B * **X-axis (both graphs):** * Label: Training Step * Scale: 0 to 150, with tick marks at 0, 30, 60, 90, 120, and 150. * **Y-axis (both graphs):** * Label: Lichess Puzzle Acc * Scale: 0.00 to 0.30, with tick marks at 0.00, 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30. * **Legend (located in the center-right of the left graph):** * Blue line: Win Rate * Gray line: Normalized Rank ### Detailed Analysis **Left Graph: Qwen2.5-7B** * **Win Rate (Blue):** * Trend: Initially increases sharply, then plateaus. * Data Points: * Training Step 0: ~0.01 * Training Step 30: ~0.12 * Training Step 60: ~0.20 * Training Step 90: ~0.28 * Training Step 120: ~0.29 * Training Step 150: ~0.29 * **Normalized Rank (Gray):** * Trend: Increases sharply, then plateaus at a slightly lower level than the Win Rate. * Data Points: * Training Step 0: ~0.01 * Training Step 30: ~0.03 * Training Step 60: ~0.23 * Training Step 90: ~0.28 * Training Step 120: ~0.29 * Training Step 150: ~0.29 **Right Graph: Llama3.1-8B** * **Win Rate (Blue):** * Trend: Increases sharply, then fluctuates around a plateau. * Data Points: * Training Step 0: ~0.01 * Training Step 30: ~0.30 * Training Step 60: ~0.29 * Training Step 90: ~0.31 * Training Step 120: ~0.33 * Training Step 150: ~0.29 * **Normalized Rank (Gray):** * Trend: Increases sharply, then fluctuates, with a noticeable dip around Training Step 120. * Data Points: * Training Step 0: ~0.01 * Training Step 30: ~0.09 * Training Step 60: ~0.23 * Training Step 90: ~0.29 * Training Step 120: ~0.26 * Training Step 150: ~0.31 ### Key Observations * Both models show a rapid initial increase in both Win Rate and Normalized Rank. * Qwen2.5-7B's performance plateaus more smoothly than Llama3.1-8B. * Llama3.1-8B exhibits more fluctuation in both metrics after the initial increase. * The Normalized Rank for Llama3.1-8B dips noticeably at Training Step 120. ### Interpretation The graphs suggest that both Qwen2.5-7B and Llama3.1-8B quickly learn to solve Lichess puzzles, as indicated by the sharp initial increase in Win Rate and Normalized Rank. However, Llama3.1-8B's fluctuating performance after the initial learning phase may indicate instability or sensitivity to specific training steps. Qwen2.5-7B appears to have a more stable learning curve, reaching a similar level of performance but without the fluctuations observed in Llama3.1-8B. The dip in Llama3.1-8B's Normalized Rank at Training Step 120 could be due to a change in the training data or some other factor affecting the model's ability to maintain its ranking.

## Line Chart: Model Performance on Lichess Puzzle Accuracy ### Overview The image presents two line charts comparing the performance of two models, Qwen2.5-7B and Llama3.1-8B, on Lichess puzzles. Both charts plot "Lichess Puzzle Acc" (Accuracy) against "Training Step". Each chart displays two data series: "Win Rate" and "Normalized Rank". ### Components/Axes * **X-axis:** "Training Step" ranging from 0 to 150. The axis is linearly scaled with markers at intervals of 30. * **Y-axis:** "Lichess Puzzle Acc" ranging from 0.00 to 0.30. The axis is linearly scaled with markers at intervals of 0.05. * **Left Chart Title:** "Qwen2.5-7B" * **Right Chart Title:** "Llama3.1-8B" * **Legend (Bottom-Left of each chart):** * "Win Rate" - Represented by a solid blue line. * "Normalized Rank" - Represented by a solid grey line. ### Detailed Analysis or Content Details **Qwen2.5-7B Chart:** * **Win Rate (Blue Line):** The line slopes sharply upward from 0 at Training Step 0, reaching approximately 0.15 at Training Step 30. It continues to rise, leveling off around 0.28-0.29 between Training Steps 90 and 150. * Step 0: 0.00 * Step 30: ~0.15 * Step 60: ~0.23 * Step 90: ~0.27 * Step 120: ~0.28 * Step 150: ~0.29 * **Normalized Rank (Grey Line):** The line also increases rapidly from 0 at Training Step 0, reaching approximately 0.12 at Training Step 30. It continues to increase, reaching approximately 0.28-0.29 between Training Steps 90 and 150. * Step 0: 0.00 * Step 30: ~0.12 * Step 60: ~0.21 * Step 90: ~0.27 * Step 120: ~0.28 * Step 150: ~0.29 **Llama3.1-8B Chart:** * **Win Rate (Blue Line):** The line starts at 0 at Training Step 0 and rises very steeply to approximately 0.28 at Training Step 30. It then fluctuates between approximately 0.26 and 0.31 between Training Steps 60 and 150. * Step 0: 0.00 * Step 30: ~0.28 * Step 60: ~0.26 * Step 90: ~0.31 * Step 120: ~0.29 * Step 150: ~0.30 * **Normalized Rank (Grey Line):** The line starts at 0 at Training Step 0 and rises steeply to approximately 0.15 at Training Step 30. It then fluctuates between approximately 0.22 and 0.28 between Training Steps 60 and 150. * Step 0: 0.00 * Step 30: ~0.15 * Step 60: ~0.22 * Step 90: ~0.26 * Step 120: ~0.25 * Step 150: ~0.28 ### Key Observations * Both models show a significant improvement in performance during the initial training steps (0-30). * Qwen2.5-7B exhibits a smoother learning curve, with a more gradual increase in both Win Rate and Normalized Rank. * Llama3.1-8B shows a more rapid initial increase, followed by fluctuations in performance. * The Win Rate consistently outperforms the Normalized Rank for both models. * Both models appear to converge in performance around Training Step 120-150. ### Interpretation The data suggests that both Qwen2.5-7B and Llama3.1-8B are effective at learning to solve Lichess puzzles. The initial rapid increase in performance indicates that the models quickly grasp the basic principles of puzzle solving. The subsequent leveling off or fluctuations suggest that the models are approaching their performance limits or are encountering more challenging puzzles. The difference in learning curves between the two models could be attributed to differences in their architectures, training data, or optimization algorithms. The smoother learning curve of Qwen2.5-7B might indicate a more stable training process, while the fluctuations in Llama3.1-8B could suggest a more sensitive or volatile training process. The consistently higher Win Rate compared to Normalized Rank suggests that the Win Rate metric is more sensitive to improvements in puzzle-solving ability. The Normalized Rank metric might be influenced by other factors, such as the difficulty of the puzzles or the performance of other players. The convergence of performance around Training Step 120-150 suggests that both models are reaching a similar level of proficiency in solving Lichess puzzles. Further training might not yield significant improvements in performance.

## Line Graphs: Qwen2.5-7B and Llama3.1-8B Performance Comparison ### Overview The image contains two side-by-side line graphs comparing the performance of two AI models (Qwen2.5-7B and Llama3.1-8B) across training steps. Both graphs track two metrics: **Win Rate** (blue line) and **Normalized Rank** (gray line) on the y-axis (Lichess Puzzle Accuracy) against **Training Steps** (0–150) on the x-axis. --- ### Components/Axes - **X-Axis**: Training Steps (0–150, linear scale). - **Y-Axis**: Lichess Puzzle Accuracy (0.00–0.30, linear scale). - **Legends**: - Blue line = Win Rate - Gray line = Normalized Rank - **Graph Titles**: - Left: "Qwen2.5-7B" - Right: "Llama3.1-8B" --- ### Detailed Analysis #### Qwen2.5-7B (Left Graph) - **Win Rate (Blue Line)**: - Starts at ~0.00 at 0 steps. - Increases steadily, reaching ~0.28 at 150 steps. - Slope: Gradual upward trend with no plateaus. - **Normalized Rank (Gray Line)**: - Starts at ~0.01 at 0 steps. - Rises sharply initially, then plateaus slightly above the blue line. - Peaks at ~0.29 at 150 steps. - **Key Relationship**: The gray line remains consistently ~0.01–0.02 higher than the blue line throughout. #### Llama3.1-8B (Right Graph) - **Win Rate (Blue Line)**: - Starts at ~0.00 at 0 steps. - Sharp upward spike after ~30 steps, reaching ~0.30 at 150 steps. - Temporary dip to ~0.26 at ~90 steps, then recovery. - **Normalized Rank (Gray Line)**: - Starts at ~0.01 at 0 steps. - Gradual rise, peaking at ~0.28 at 150 steps. - Crossed by the blue line after ~60 steps. - **Key Relationship**: Blue line overtakes gray line after ~60 steps, indicating Win Rate surpasses Normalized Rank. --- ### Key Observations 1. **Qwen2.5-7B**: - Win Rate and Normalized Rank trends are closely aligned but never intersect. - Both metrics plateau near 0.28–0.29 by 150 steps. 2. **Llama3.1-8B**: - Win Rate accelerates faster than Normalized Rank, overtaking it after ~60 steps. - Temporary dip in Win Rate at ~90 steps suggests instability or optimization challenges. 3. **General Trend**: - Both models show improvement with training steps, but Llama3.1-8B demonstrates sharper gains in Win Rate. --- ### Interpretation - **Performance Insights**: - Llama3.1-8B’s faster Win Rate growth suggests superior puzzle-solving efficiency in later training phases. - Qwen2.5-7B’s stable but slower progression indicates consistent but less aggressive learning. - **Anomalies**: - Llama3.1-8B’s Win Rate dip at ~90 steps may reflect overfitting or resource constraints during training. - **Implications**: - For applications prioritizing rapid performance gains, Llama3.1-8B may be preferable. - Qwen2.5-7B’s stability could be advantageous for tasks requiring consistent, incremental improvement. --- ### Spatial Grounding & Validation - Legends are positioned at the bottom-left of each graph, matching line colors (blue/gray). - Axis labels and titles are clearly separated from data regions. - All numerical values align with visual trends (e.g., Llama’s blue line overtaking gray line post-60 steps).