## Line Chart: Benchmark: OlympiadBench ### Overview The image is a line chart comparing the validation scores of two models, GRPO and MEL, over training steps. The chart displays the performance of each model as training progresses, allowing for a visual comparison of their learning curves. ### Components/Axes * **Title:** Benchmark: OlympiadBench * **X-axis:** Training Step (ranging from 0 to 140, with increments of 20) * **Y-axis:** Validation Score (ranging from 0.44 to 0.54, with 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):** * Trend: Generally increasing with fluctuations. * Data Points: * Training Step 0: Validation Score ~0.445 * Training Step 20: Validation Score ~0.435 * Training Step 40: Validation Score ~0.47 * Training Step 60: Validation Score ~0.50 * Training Step 80: Validation Score ~0.52 * Training Step 100: Validation Score ~0.54 * Training Step 120: Validation Score ~0.525 * Training Step 140: Validation Score ~0.535 * **MEL (pink line):** * Trend: Generally increasing with fluctuations. * Data Points: * Training Step 0: Validation Score ~0.445 * Training Step 20: Validation Score ~0.45 * Training Step 40: Validation Score ~0.50 * Training Step 60: Validation Score ~0.53 * Training Step 80: Validation Score ~0.52 * Training Step 100: Validation Score ~0.54 * Training Step 120: Validation Score ~0.55 * Training Step 140: Validation Score ~0.53 ### Key Observations * Both models show an overall increasing trend in validation score as the training step increases. * MEL appears to have a slightly higher validation score than GRPO at several points, particularly around training step 120. * Both models exhibit fluctuations in their validation scores, indicating some variability in their learning process. ### Interpretation The chart illustrates the learning curves of the GRPO and MEL models on the OlympiadBench benchmark. The increasing validation scores suggest that both models are learning effectively as training progresses. The fluctuations in the curves may indicate sensitivity to specific training examples or the need for further optimization. The slightly higher validation scores of MEL at certain points suggest that it may be a more effective model for this particular benchmark, although the difference is not substantial. Further analysis, including statistical significance testing, would be needed to confirm this.

\n ## Line Chart: Validation Score vs. Training Step (OlympiadBench) ### Overview This image presents a line chart illustrating the validation score of two models, GRP0 and MEL, as a function of the training step. The chart appears to track the performance of these models during a training process on the OlympiadBench benchmark. ### Components/Axes * **Title:** Benchmark: OlympiadBench (positioned at the top-center) * **X-axis:** Training Step (ranging from approximately 0 to 140, with markers at intervals of 20) * **Y-axis:** Validation Score (ranging from approximately 0.44 to 0.55, with markers at intervals of 0.02) * **Legend:** Located in the top-right corner. * GRP0 (represented by a light blue line) * MEL (represented by a light red/pink line) * **Gridlines:** Present to aid in reading values. ### Detailed Analysis **GRP0 (Light Blue Line):** The GRP0 line generally slopes upward from step 0 to approximately step 100, then exhibits some fluctuation before leveling off. * Step 0: Approximately 0.44 * Step 20: Approximately 0.46 * Step 40: Approximately 0.48 * Step 60: Approximately 0.50 * Step 80: Approximately 0.47 * Step 100: Approximately 0.53 * Step 120: Approximately 0.52 * Step 140: Approximately 0.53 **MEL (Light Red/Pink Line):** The MEL line also generally slopes upward, but with more pronounced fluctuations. * Step 0: Approximately 0.45 * Step 20: Approximately 0.43 * Step 40: Approximately 0.50 * Step 60: Approximately 0.52 * Step 80: Approximately 0.52 * Step 100: Approximately 0.54 * Step 120: Approximately 0.55 * Step 140: Approximately 0.54 ### Key Observations * Both models show an increasing trend in validation score with increasing training steps, indicating learning. * The MEL model appears to achieve a slightly higher maximum validation score (around 0.55) compared to the GRP0 model (around 0.53). * The MEL model exhibits more volatility in its validation score during training. * Both models appear to converge towards a stable validation score after approximately 100 training steps. ### Interpretation The chart demonstrates the training progress of two models (GRP0 and MEL) on the OlympiadBench benchmark. The increasing validation scores suggest that both models are learning and improving their performance over time. The MEL model appears to be slightly more effective, reaching a higher peak validation score, but also exhibits greater instability during training. This could indicate a higher sensitivity to training data or a more complex learning process. The convergence of both lines towards the end of the training period suggests that further training may yield diminishing returns. The fluctuations in the validation scores could be due to factors such as the batch size, learning rate, or the inherent difficulty of the OlympiadBench benchmark. The data suggests that the MEL model is a better performer, but requires more careful tuning to avoid instability.

## Line Chart: Benchmark: OlympiadBench ### Overview The image displays a line chart comparing the validation score performance of two methods, GRPO and MEL, over the course of training steps on a benchmark named "OlympiadBench". The chart tracks performance from step 0 to approximately step 140. ### Components/Axes * **Chart Title:** "Benchmark: OlympiadBench" (Top center) * **Y-Axis:** * **Label:** "Validation Score" (Left side, rotated vertically) * **Scale:** Linear scale ranging from 0.44 to 0.54, with major tick marks at 0.02 intervals (0.44, 0.46, 0.48, 0.50, 0.52, 0.54). * **X-Axis:** * **Label:** "Training Step" (Bottom center) * **Scale:** Linear scale from 0 to 140, with major tick marks every 20 steps (0, 20, 40, 60, 80, 100, 120, 140). * **Legend:** Located in the bottom-right corner of the plot area. * **GRPO:** Represented by a blue line with circular markers. * **MEL:** Represented by a red line with triangular markers. ### Detailed Analysis **Data Series Trends & Approximate Points:** 1. **GRPO (Blue Line, Circles):** * **Trend:** Starts at the lowest point, experiences an initial dip, then follows a generally upward trend with moderate fluctuations. It shows a significant dip around step 60 before recovering. * **Approximate Data Points:** * Step 0: ~0.445 * Step 10: ~0.450 * Step 20: ~0.440 (Local minimum) * Step 30: ~0.455 * Step 40: ~0.470 * Step 50: ~0.500 * Step 60: ~0.480 (Significant dip) * Step 70: ~0.515 * Step 80: ~0.530 * Step 90: ~0.525 * Step 100: ~0.540 (Peak) * Step 110: ~0.535 * Step 120: ~0.520 * Step 130: ~0.535 * Step 140: ~0.535 2. **MEL (Red Line, Triangles):** * **Trend:** Starts higher than GRPO, dips early, then exhibits a strong upward trend with higher volatility (larger swings up and down) compared to GRPO. It achieves the highest overall score on the chart. * **Approximate Data Points:** * Step 0: ~0.450 * Step 10: ~0.445 (Local minimum) * Step 20: ~0.460 * Step 30: ~0.510 * Step 40: ~0.500 * Step 50: ~0.520 * Step 60: ~0.510 * Step 70: ~0.525 * Step 80: ~0.520 * Step 90: ~0.530 * Step 100: ~0.540 * Step 110: ~0.530 * Step 120: ~0.545 (Highest point on chart) * Step 130: ~0.530 * Step 140: ~0.540 ### Key Observations 1. **Overall Improvement:** Both GRPO and MEL show a clear positive trend, indicating that validation scores improve with increased training steps on the OlympiadBench benchmark. 2. **Performance Crossover:** MEL starts with a slight advantage, but GRPO catches up and briefly surpasses it around step 80. The lines cross multiple times, indicating competitive performance. 3. **Volatility Difference:** The MEL series (red) exhibits greater volatility, with sharper peaks and troughs, particularly between steps 20-40 and 110-130. The GRPO series (blue) is comparatively smoother, with its most notable deviation being the dip at step 60. 4. **Peak Performance:** The highest recorded validation score (~0.545) is achieved by MEL at approximately step 120. Both methods end the tracked period (step 140) at a similar high level (~0.535-0.540). 5. **Initial Phase:** Both methods experience a performance dip within the first 20 training steps before beginning their sustained ascent. ### Interpretation The chart demonstrates a comparative learning curve analysis for two algorithms on a challenging benchmark. The data suggests that while both methods are effective, they exhibit different learning dynamics: * **MEL** may have a higher potential for peak performance (as seen at step 120) but comes with less stability during training, as evidenced by its larger fluctuations. This could imply sensitivity to specific training batches or a more aggressive optimization strategy. * **GRPO** appears to be a more stable learner. Its significant dip at step 60 is an anomaly that warrants investigation—it could correspond to a difficult subset of data, a learning rate issue, or a temporary instability in the optimization process. Its recovery from this dip shows robustness. The fact that both methods converge to a similar final performance range suggests that for this specific benchmark, the choice between them might depend on secondary factors: if consistent, predictable progress is valued, GRPO might be preferred. If the training process can tolerate volatility in pursuit of potentially higher interim peaks, MEL could be the candidate. The initial dips for both models are curious and might indicate a common challenge in the early phase of learning for this task, such as overcoming a local minimum or adapting from a pre-trained state.

## Line Chart: Benchmark: OlympiadBench ### Overview The chart compares the validation scores of two methods, GRPO and MEL, across training steps (0–140) on the OlympiadBench benchmark. Both lines show an upward trend, with GRPO (blue) and MEL (pink) converging at higher scores as training progresses. ### Components/Axes - **X-axis (Training Step)**: Labeled "Training Step," with markers at 0, 20, 40, 60, 80, 100, 120, and 140. - **Y-axis (Validation Score)**: Labeled "Validation Score," ranging from 0.44 to 0.54 in increments of 0.02. - **Legend**: Located in the bottom-right corner, associating: - **Blue circles**: GRPO - **Pink triangles**: MEL ### Detailed Analysis #### GRPO (Blue Line) - **Trend**: Starts at 0.44 (step 0), dips to 0.43 (step 10), then rises steadily to 0.53 (step 100), fluctuates slightly, and stabilizes near 0.53–0.54 by step 140. - **Key Data Points**: - Step 0: 0.44 - Step 10: 0.43 - Step 20: 0.45 - Step 40: 0.47 - Step 60: 0.48 - Step 80: 0.53 - Step 100: 0.54 - Step 120: 0.53 - Step 140: 0.53 #### MEL (Pink Line) - **Trend**: Begins at 0.44 (step 0), rises sharply to 0.51 (step 20), fluctuates between 0.50–0.54, peaks at 0.55 (step 120), then dips slightly to 0.54 (step 140). - **Key Data Points**: - Step 0: 0.44 - Step 10: 0.45 - Step 20: 0.51 - Step 40: 0.49 - Step 60: 0.52 - Step 80: 0.52 - Step 100: 0.54 - Step 120: 0.55 - Step 140: 0.54 ### Key Observations 1. **Initial Divergence**: MEL starts with a sharper increase than GRPO (e.g., 0.44 → 0.51 at step 20 vs. GRPO’s 0.44 → 0.45). 2. **Convergence**: Both methods plateau near 0.53–0.54 by step 100, with MEL briefly exceeding GRPO at step 120 (0.55 vs. 0.53). 3. **Volatility**: MEL exhibits more fluctuations (e.g., drop from 0.54 to 0.52 at step 80), while GRPO shows steadier growth. 4. **Final Scores**: At step 140, both methods achieve ~0.53–0.54, with MEL marginally higher. ### Interpretation The data suggests that both GRPO and MEL improve validation performance with training, but MEL demonstrates higher variability and occasional peaks. The convergence at later steps implies similar efficacy for complex tasks, though MEL’s transient superiority at step 120 may indicate sensitivity to specific training dynamics. The initial dip in GRPO’s score (step 10) could reflect an adaptation phase. Overall, the chart highlights trade-offs between stability (GRPO) and exploratory performance (MEL) in optimization benchmarks.