## Line Chart: Benchmark Average ### Overview The image is a line chart comparing the validation scores of two models, GRPO and MEL, over training steps. The chart shows how the validation score changes as the models are trained. ### Components/Axes * **Title:** Benchmark: Average * **X-axis:** Training Step (ranging from 0 to 140 in increments of 20) * **Y-axis:** Validation Score (ranging from 0.36 to 0.46 in increments of 0.02) * **Legend:** Located in the bottom-right corner. * GRPO (blue line with circle markers) * MEL (pink line with triangle markers) ### Detailed Analysis * **GRPO (blue line):** * Starts at approximately 0.36. * Increases to approximately 0.405 at Training Step 20. * Decreases to approximately 0.38 at Training Step 30. * Increases to approximately 0.41 at Training Step 80. * Peaks at approximately 0.44 at Training Step 100. * Decreases and plateaus around 0.42 between Training Steps 110 and 130. * Ends at approximately 0.415 at Training Step 140. * **MEL (pink line):** * Starts at approximately 0.36. * Increases to approximately 0.405 at Training Step 20. * Decreases slightly to approximately 0.403 at Training Step 30. * Generally increases to approximately 0.445 at Training Step 100. * Dips slightly to approximately 0.44 at Training Step 120. * Peaks at approximately 0.46 at Training Step 130. * Ends at approximately 0.455 at Training Step 140. ### Key Observations * Both models start with the same validation score. * MEL generally outperforms GRPO after Training Step 60. * MEL reaches a higher peak validation score than GRPO. * GRPO's validation score plateaus towards the end of the training steps. ### Interpretation The chart compares the performance of two models, GRPO and MEL, during training. The validation score is used as a metric to evaluate the models' performance. The data suggests that MEL generally performs better than GRPO, especially in the later stages of training. MEL achieves a higher peak validation score, indicating better generalization performance on the validation set. GRPO's performance plateaus, suggesting it may have reached its learning capacity or is overfitting to the training data. The initial similar performance suggests both models may have similar initial learning capabilities, but MEL is better at leveraging the training data to improve its performance over time.

\n ## Line Chart: Benchmark Average Validation Score vs. Training Step ### Overview This image presents a line chart illustrating the validation score of two models, GRPO and MEL, as a function of the training step. The chart aims to compare the performance of these models during the training process. ### Components/Axes * **Title:** "Benchmark: Average" - positioned at the top-center of the chart. * **X-axis:** "Training Step" - ranging from approximately 0 to 140, with markers at intervals of 20. * **Y-axis:** "Validation Score" - ranging from approximately 0.36 to 0.46, with markers at intervals of 0.02. * **Legend:** Located in the top-right corner of the chart. * GRPO - represented by a blue line with circular markers. * MEL - represented by a light-red line with triangular markers. * **Gridlines:** Faint gray horizontal and vertical gridlines are present to aid in reading values. ### Detailed Analysis **GRPO (Blue Line):** The GRPO line generally slopes upward from step 0 to approximately step 100, then plateaus and slightly declines. * At Training Step 0, Validation Score is approximately 0.37. * At Training Step 20, Validation Score is approximately 0.41. * At Training Step 40, Validation Score is approximately 0.38. * At Training Step 60, Validation Score is approximately 0.41. * At Training Step 80, Validation Score is approximately 0.40. * At Training Step 100, Validation Score is approximately 0.44. * At Training Step 120, Validation Score is approximately 0.43. * At Training Step 140, Validation Score is approximately 0.42. **MEL (Light-Red Line):** The MEL line also generally slopes upward, but with more pronounced fluctuations. * At Training Step 0, Validation Score is approximately 0.36. * At Training Step 20, Validation Score is approximately 0.41. * At Training Step 40, Validation Score is approximately 0.39. * At Training Step 60, Validation Score is approximately 0.42. * At Training Step 80, Validation Score is approximately 0.42. * At Training Step 100, Validation Score is approximately 0.45. * At Training Step 120, Validation Score is approximately 0.44. * At Training Step 140, Validation Score is approximately 0.46. ### Key Observations * Both models show an increasing trend in validation score with increasing training steps, indicating learning. * The MEL model consistently achieves a slightly higher validation score than the GRPO model, especially in the later stages of training (after step 80). * The GRPO model exhibits more volatility in its validation score, with larger fluctuations between training steps. * The MEL model reaches its peak validation score at the final training step (140). ### Interpretation The chart suggests that the MEL model is performing better than the GRPO model on the benchmark task, as evidenced by its consistently higher validation scores. The increasing trend for both models indicates that both are learning from the training data. The fluctuations in the GRPO model's validation score could indicate instability during training or sensitivity to specific training batches. The fact that MEL continues to improve until the final training step suggests that further training might yield even better results. The "Benchmark: Average" title implies that the validation scores are averaged across a set of benchmark tests, providing a more robust measure of model performance. The data suggests that MEL is a more stable and effective model for this particular benchmark.

## 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 suggests both methods improve over time, but with different patterns and final outcomes. ### Components/Axes * **Chart Title:** "Benchmark: Average" (centered at the top). * **Y-Axis:** Labeled "Validation Score." The scale runs from approximately 0.36 to 0.46, with major gridlines at intervals of 0.02 (0.36, 0.38, 0.40, 0.42, 0.44, 0.46). * **X-Axis:** Labeled "Training Step." The scale runs from 0 to 140, with major tick marks and labels at intervals of 20 (0, 20, 40, 60, 80, 100, 120, 140). * **Legend:** Located in the bottom-right corner of the plot area. * A blue line with circle markers is labeled "GRPO". * An orange dashed line with triangle markers is labeled "MEL". * **Data Series:** 1. **GRPO (Blue, solid line, circle markers):** This line shows significant volatility. It starts low, rises, dips sharply around step 30, recovers, dips again around step 70, peaks near step 90, and then declines towards the end. 2. **MEL (Orange, dashed line, triangle markers):** This line shows a more consistent upward trend with less severe dips. It starts at a similar point to GRPO, generally climbs with minor fluctuations, and reaches its highest point near the end of the plotted steps. ### Detailed Analysis **Trend Verification & Data Point Extraction (Approximate Values):** | Training Step | GRPO (Blue) | MEL (Orange) | | :--- | :--- | :--- | | 0 | ~0.36 | ~0.36 | | 10 | ~0.39 | ~0.385 | | 20 | ~0.405 | ~0.405 | | 30 | ~0.38 (sharp dip) | ~0.405 | | 40 | ~0.40 | ~0.395 (minor dip) | | 50 | ~0.39 | ~0.42 | | 60 | ~0.415 | ~0.425 | | 70 | ~0.395 (second dip) | ~0.43 | | 80 | ~0.435 | ~0.435 | | 90 | ~0.44 (peak) | ~0.42 (dip) | | 100 | ~0.425 | ~0.44 | | 110 | ~0.425 | ~0.44 | | 120 | ~0.425 | ~0.445 | | 130 | ~0.415 | ~0.46 (peak) | ### Key Observations 1. **Final Performance Divergence:** By step 130, the MEL method (orange) achieves a significantly higher validation score (~0.46) compared to GRPO (blue, ~0.415). 2. **Volatility vs. Stability:** The GRPO line is characterized by sharp, V-shaped dips (at steps ~30 and ~70), indicating periods of performance regression during training. The MEL line is more stable, with shallower dips. 3. **Peak Timing:** GRPO peaks earlier (around step 90) and then declines. MEL's peak is at the latest measured point (step 130), suggesting it may still be improving. 4. **Initial Convergence:** Both methods start at nearly the same point (~0.36) and track closely until approximately step 25, after which their paths begin to diverge more noticeably. ### Interpretation The chart demonstrates a comparative benchmark between two training methods or algorithms (GRPO and MEL). The data suggests that while both methods learn and improve from the same starting point, **MEL exhibits more robust and sustained learning**. Its higher final score and lower volatility imply it may be a more reliable or effective optimization strategy for this particular task, avoiding the significant performance collapses seen in GRPO. The late peak of MEL also hints at potential for further improvement beyond step 130, whereas GRPO appears to have plateaued and begun to degrade, possibly indicating overfitting or instability in its later training stages. The key takeaway is that MEL's learning trajectory is both more stable and ultimately more successful within the observed training window.

## Line Graph: Benchmark: Average ### Overview The image is a line graph comparing the validation scores of two models, GRPO and MEL, across training steps. The x-axis represents training steps (0–140), and the y-axis represents validation scores (0.36–0.46). Two lines are plotted: a blue line for GRPO and a red line for MEL. ### Components/Axes - **Title**: "Benchmark: Average" - **X-axis**: "Training Step" (0–140, increments of 20) - **Y-axis**: "Validation Score" (0.36–0.46, increments of 0.02) - **Legend**: Located in the bottom-right corner, with: - Blue circle labeled "GRPO" - Red triangle labeled "MEL" ### Detailed Analysis #### GRPO (Blue Line) - **Data Points**: - 0: 0.36 - 20: 0.38 - 40: 0.38 - 60: 0.40 - 80: 0.41 - 100: 0.43 - 120: 0.42 - 140: 0.41 - **Trend**: Starts at 0.36, rises to a peak of 0.43 at step 100, then declines to 0.41 by step 140. Shows moderate fluctuations. #### MEL (Red Line) - **Data Points**: - 0: 0.36 - 20: 0.39 - 40: 0.40 - 60: 0.41 - 80: 0.42 - 100: 0.43 - 120: 0.44 - 140: 0.45 - **Trend**: Starts at 0.36, steadily increases to 0.45 by step 140. Shows consistent upward growth with minimal fluctuations. ### Key Observations 1. **MEL Outperforms GRPO**: MEL consistently achieves higher validation scores across most training steps, especially after step 80. 2. **GRPO Volatility**: GRPO exhibits sharper fluctuations, with a peak at step 100 followed by a decline. 3. **Final Scores**: At step 140, MEL reaches 0.45, while GRPO drops to 0.41. ### Interpretation The graph suggests that MEL demonstrates more stable and effective learning over training steps compared to GRPO. While GRPO briefly surpasses MEL around step 100, its subsequent decline indicates potential instability or overfitting. MEL’s steady ascent implies robust performance, making it the superior model for this benchmark. The divergence in trends highlights differences in optimization strategies or architectural strengths between the two models.