## Line Chart: Benchmark Average ### Overview The image is a line chart comparing the validation scores of two methods, GRPO and MEL, over training steps. The chart shows how the validation score changes as the training progresses for each method. ### Components/Axes * **Title:** Benchmark: Average * **X-axis:** Training Step, ranging from 0 to 140 in increments of 20. * **Y-axis:** Validation Score, ranging from 0.425 to 0.600. * **Legend:** Located in the bottom-left corner. * GRPO (blue line with circle markers) * MEL (pink line with triangle markers) ### Detailed Analysis * **GRPO (blue line):** * Starts at approximately 0.41. * Decreases to approximately 0.45 at step 10. * Decreases to approximately 0.43 at step 20. * Increases to approximately 0.48 at step 40. * Increases to approximately 0.51 at step 50. * Increases to approximately 0.53 at step 60. * Decreases to approximately 0.52 at step 70. * Increases to approximately 0.54 at step 80. * Decreases to approximately 0.53 at step 90. * Increases to approximately 0.56 at step 100. * Decreases to approximately 0.55 at step 110. * Increases to approximately 0.56 at step 120. * Decreases to approximately 0.55 at step 130. * Increases to approximately 0.56 at step 140. * **MEL (pink line):** * Starts at approximately 0.41. * Increases to approximately 0.47 at step 20. * Increases to approximately 0.51 at step 40. * Increases to approximately 0.55 at step 50. * Increases to approximately 0.56 at step 60. * Decreases to approximately 0.54 at step 70. * Increases to approximately 0.56 at step 80. * Decreases to approximately 0.55 at step 90. * Increases to approximately 0.56 at step 100. * Increases to approximately 0.58 at step 120. * Increases to approximately 0.61 at step 140. ### Key Observations * Both GRPO and MEL start with similar validation scores. * MEL generally outperforms GRPO after the initial training steps. * MEL shows a more consistent upward trend, especially towards the end of the training. * GRPO fluctuates more than MEL throughout the training process. ### Interpretation The chart suggests that MEL is a more effective method for this particular benchmark, as it achieves higher validation scores and demonstrates a more stable and upward trend compared to GRPO. The fluctuations in GRPO's performance indicate that it might be more sensitive to the training process or require further optimization. The data implies that MEL converges to a better solution more consistently than GRPO.

\n ## Line Chart: Benchmark Average ### Overview The image presents a line chart comparing the validation score of two models, GRP0 and MEL, across 140 training steps. The chart visualizes the performance of each model during the training process, showing how the validation score changes with each step. ### Components/Axes * **Title:** "Benchmark: Average" - positioned at the top-center of the chart. * **X-axis:** "Training Step" - ranging from 0 to 140, with tick marks at intervals of 20. * **Y-axis:** "Validation Score" - ranging from approximately 0.42 to 0.62, with tick marks at intervals of 0.025. * **Legend:** Located in the top-right corner of the chart. * GRP0 - represented by a light blue line with circular markers. * MEL - represented by a light red/pink line with triangular markers. * **Gridlines:** Horizontal and vertical gridlines are present to aid in reading values. ### Detailed Analysis **GRP0 (Light Blue Line):** The GRP0 line generally slopes upward, indicating an increasing validation score over the training steps. * At Training Step 0, the Validation Score is approximately 0.43. * At Training Step 20, the Validation Score drops to approximately 0.38. * At Training Step 40, the Validation Score rises to approximately 0.48. * At Training Step 60, the Validation Score is approximately 0.53. * At Training Step 80, the Validation Score dips to approximately 0.52. * At Training Step 100, the Validation Score reaches approximately 0.56. * At Training Step 120, the Validation Score is approximately 0.57. * At Training Step 140, the Validation Score decreases to approximately 0.55. **MEL (Light Red/Pink Line):** The MEL line also generally slopes upward, but with more fluctuations. * At Training Step 0, the Validation Score is approximately 0.32. * At Training Step 20, the Validation Score rises to approximately 0.52. * At Training Step 40, the Validation Score is approximately 0.55. * At Training Step 60, the Validation Score is approximately 0.57. * At Training Step 80, the Validation Score dips to approximately 0.53. * At Training Step 100, the Validation Score rises to approximately 0.56. * At Training Step 120, the Validation Score is approximately 0.59. * At Training Step 140, the Validation Score reaches approximately 0.61. ### Key Observations * The MEL model consistently achieves higher validation scores than the GRP0 model throughout the training process. * Both models exhibit some degree of fluctuation in their validation scores, suggesting that the training process is not perfectly smooth. * The GRP0 model experiences a significant drop in validation score at Training Step 20, followed by a recovery. * The MEL model shows a more consistent upward trend, with smaller fluctuations. * Both models appear to be converging towards a stable validation score as the training progresses, but the MEL model is still improving at the final step. ### Interpretation The chart demonstrates the learning curves of two models, GRP0 and MEL, during a training process. The validation score serves as a metric for evaluating the model's performance on unseen data. The consistently higher validation scores of the MEL model suggest that it is a better-performing model than GRP0, at least for this benchmark. The fluctuations in the validation scores indicate that the training process is sensitive to the specific training data and that further optimization may be possible. The convergence of the learning curves towards the end of the training process suggests that both models are approaching their maximum performance potential. The initial dip in GRP0's performance could be due to a challenging initial batch of data or a learning rate adjustment. The continued improvement of MEL at the final step suggests that it may benefit from further training.

## Line Chart: Benchmark: Average ### Overview The image displays a line chart comparing the performance of two methods, labeled "GRPO" and "MEL," over the course of training. The chart plots a "Validation Score" against "Training Step," showing how each method's performance evolves. The overall trend for both methods is upward, indicating improvement with more training steps, but their trajectories and final performance levels differ. ### Components/Axes * **Chart Title:** "Benchmark: Average" (centered at the top). * **Y-Axis:** * **Label:** "Validation Score" (rotated vertically on the left). * **Scale:** Linear scale ranging from approximately 0.425 to 0.600. * **Major Tick Marks:** Labeled at 0.425, 0.450, 0.475, 0.500, 0.525, 0.550, 0.575, 0.600. * **X-Axis:** * **Label:** "Training Step" (centered at the bottom). * **Scale:** Linear scale from 0 to 140. * **Major Tick Marks:** Labeled at 0, 20, 40, 60, 80, 100, 120, 140. * **Legend:** * **Position:** Bottom-right corner of the chart area. * **Entries:** 1. **GRPO:** Represented by a blue line with circular markers. 2. **MEL:** Represented by a red line with circular markers. * **Grid:** Light gray horizontal and vertical grid lines are present, aligning with the major tick marks on both axes. ### Detailed Analysis **Data Series: GRPO (Blue Line)** * **Trend:** The line shows an overall upward trend but with notable volatility. It begins with a sharp initial rise, experiences a significant dip, recovers, and then continues a generally increasing but fluctuating path. * **Approximate Data Points:** * Step 0: ~0.425 * Step 10: ~0.450 * Step 20: ~0.440 (local dip) * Step 30: ~0.425 (lowest point after start) * Step 40: ~0.475 * Step 50: ~0.500 * Step 60: ~0.525 * Step 70: ~0.510 (dip) * Step 80: ~0.525 * Step 90: ~0.520 (dip) * Step 100: ~0.550 * Step 110: ~0.560 * Step 120: ~0.550 (dip) * Step 130: ~0.560 * Step 140: ~0.575 **Data Series: MEL (Red Line)** * **Trend:** The line shows a strong, consistent upward trend with only minor fluctuations. It starts higher than GRPO and maintains a lead throughout, with the performance gap widening significantly in the later stages of training. * **Approximate Data Points:** * Step 0: ~0.450 * Step 10: ~0.450 * Step 20: ~0.475 * Step 30: ~0.510 * Step 40: ~0.525 * Step 50: ~0.550 * Step 60: ~0.550 * Step 70: ~0.560 * Step 80: ~0.550 (minor dip) * Step 90: ~0.560 * Step 100: ~0.575 * Step 110: ~0.575 * Step 120: ~0.590 * Step 130: ~0.600 * Step 140: ~0.610 (highest point on chart) ### Key Observations 1. **Performance Gap:** The MEL method (red) consistently achieves a higher Validation Score than the GRPO method (blue) after the initial training steps (around step 10). 2. **Volatility vs. Stability:** The GRPO line exhibits more pronounced dips and recoveries (e.g., at steps 20, 70, 90, 120), suggesting less stable training. The MEL line is smoother, indicating more stable and reliable improvement. 3. **Divergence:** The performance gap between the two methods widens considerably after step 80. By step 140, MEL's score (~0.610) is approximately 0.035 points higher than GRPO's (~0.575). 4. **Final Trajectory:** At the final recorded step (140), the MEL line is still on a clear upward trajectory, while the GRPO line appears to be plateauing or rising more slowly. ### Interpretation This chart demonstrates a comparative benchmark of two training methodologies. The data strongly suggests that the **MEL method is superior to the GRPO method** for this specific task, based on two key metrics: * **Higher Final Performance:** MEL achieves a significantly higher validation score. * **More Stable Learning:** MEL's learning curve is smoother and more consistent, which is often desirable in machine learning as it indicates robustness and predictability. The widening gap in later training stages implies that MEL may have a better capacity for continued learning or generalization as training progresses. The volatility in the GRPO curve could indicate sensitivity to hyperparameters, batch composition, or other training instabilities. For a technical document, this chart provides clear empirical evidence favoring the adoption of the MEL approach over GRPO for the benchmarked task, assuming validation score is the primary success metric.

## Line Graph: Benchmark: Average ### Overview The image is a line graph comparing the validation scores of two models, GRPO (blue) and MEL (pink), across training steps. The x-axis represents training steps (0–140), and the y-axis represents validation scores (0.425–0.600). Both lines show upward trends, with MEL consistently outperforming GRPO after ~60 training steps. ### Components/Axes - **Title**: "Benchmark: Average" (top center). - **X-axis**: "Training Step" (0–140, increments of 20). - **Y-axis**: "Validation Score" (0.425–0.600, increments of 0.025). - **Legend**: Bottom-right corner, with: - Blue circle: "GRPO" - Pink triangle: "MEL" - **Grid**: Light gray grid lines for reference. ### Detailed Analysis 1. **GRPO (Blue Line)**: - Starts at ~0.45 (step 0). - Dips to ~0.425 at step 30. - Rises to ~0.575 at step 100. - Ends at ~0.57 (step 140). - Fluctuations: Minor dips at steps 30 and 90. 2. **MEL (Pink Line)**: - Starts at ~0.425 (step 0). - Rises steadily to ~0.55 at step 60. - Peaks at ~0.600 by step 140. - Fluctuations: Slight dip at step 80 (~0.54). ### Key Observations - **MEL outperforms GRPO** after ~60 training steps, with a steeper slope. - **GRPO** experiences a temporary dip at step 30 but recovers by step 60. - Both models plateau near ~0.57 by step 140, but MEL maintains a higher score throughout. ### Interpretation The graph demonstrates that MEL achieves higher validation scores than GRPO as training progresses, suggesting superior performance in this benchmark. The GRPO model shows resilience after an early dip, but MEL’s consistent growth indicates better optimization. The final scores (~0.57 for both) imply that while MEL is more effective, GRPO remains competitive with potential for further improvement. The trends highlight the importance of training duration and model architecture in achieving optimal validation performance.