## Chart: Performance Comparison of GRPO and AutoGen Team ### Overview The image is a line chart comparing the performance of a "Proposed (GRPO)" method against an "AutoGen Team" method across two metrics: "Writing Quality" and "Coding Pass Rate." The chart plots these metrics against "Training Steps (x10^3)." The y-axis represents "Score / Pass Rate (%)". ### Components/Axes * **X-axis:** Training Steps (x10^3). Scale ranges from 0 to 200, with tick marks at 0, 25, 50, 75, 100, 125, 150, 175, and 200. * **Y-axis:** Score / Pass Rate (%). Scale ranges from 50% to 90%, with tick marks at 50, 60, 70, 80, and 90. * **Legend:** Located at the bottom of the chart. * Yellow: Writing Quality - Proposed (GRPO) * Light Blue: Writing Quality - AutoGen Team * Dark Blue: Coding Pass Rate - Proposed (GRPO) * Light Yellow: Coding Pass Rate - AutoGen Team ### Detailed Analysis * **Writing Quality - Proposed (GRPO) (Yellow):** The line starts at approximately 80% at 0 training steps and increases to approximately 95% at 200 training steps. The rate of increase slows down as the number of training steps increases. * **Writing Quality - AutoGen Team (Light Blue):** The line starts at approximately 78% at 0 training steps and increases to approximately 89% at 200 training steps. The rate of increase slows down as the number of training steps increases. * **Coding Pass Rate - Proposed (GRPO) (Dark Blue):** The line starts at approximately 55% at 0 training steps and increases to approximately 75% at 200 training steps. The rate of increase slows down as the number of training steps increases. * **Coding Pass Rate - AutoGen Team (Light Yellow):** The line starts at approximately 52% at 0 training steps and increases to approximately 62% at 200 training steps. The rate of increase slows down as the number of training steps increases. ### Key Observations * For both Writing Quality and Coding Pass Rate, the "Proposed (GRPO)" method consistently outperforms the "AutoGen Team" method. * Writing Quality scores are higher than Coding Pass Rate scores for both methods. * The performance improvement (increase in Score/Pass Rate) is more significant in the initial training steps and gradually plateaus as the number of training steps increases. ### Interpretation The chart suggests that the "Proposed (GRPO)" method is more effective than the "AutoGen Team" method for both writing quality and coding pass rate. The diminishing returns observed with increasing training steps indicate that there is a point beyond which further training yields minimal improvement in performance. The GRPO method shows a significant advantage, particularly in writing quality, suggesting it may have a more robust or efficient learning mechanism compared to the AutoGen Team method. The difference in performance between writing quality and coding pass rate may reflect the relative complexity or inherent difficulty of these tasks.

\n ## Line Chart: Performance Comparison of Proposed (GRPO) and AutoGen Team ### Overview This line chart compares the performance of a "Proposed (GRPO)" method against an "AutoGen Team" method across two metrics: "Writing Quality" and "Coding Pass Rate". Performance is measured as a percentage ("Score / Pass Rate (%)") over "Training Steps" (expressed as multiples of 10^3). ### Components/Axes * **X-axis:** Training Steps (x10^3), ranging from 0 to 200, with tick marks at 0, 25, 50, 75, 100, 125, 150, and 200. * **Y-axis:** Score / Pass Rate (%), ranging from 50% to 90%, with tick marks at 50, 60, 70, 80, and 90. * **Legend:** Located in the bottom-right corner. Contains the following entries: * Yellow Line: "Writing Quality – Proposed (GRPO)" * Blue Line: "Writing Quality – AutoGen Team" * Light Green Line: "Coding Pass Rate – Proposed (GRPO)" * Light Blue Line: "Coding Pass Rate – AutoGen Team" ### Detailed Analysis **Writing Quality – Proposed (GRPO) (Yellow Line):** The yellow line shows an upward trend, starting at approximately 68% at 0 training steps. It increases rapidly initially, then plateaus. * At 25 training steps: ~75% * At 50 training steps: ~82% * At 75 training steps: ~87% * At 100 training steps: ~89% * At 125 training steps: ~90% * At 150 training steps: ~91% * At 200 training steps: ~91% **Writing Quality – AutoGen Team (Blue Line):** The blue line also shows an upward trend, but it is less steep than the yellow line. It starts at approximately 62% at 0 training steps. * At 25 training steps: ~66% * At 50 training steps: ~72% * At 75 training steps: ~76% * At 100 training steps: ~78% * At 125 training steps: ~80% * At 150 training steps: ~82% * At 200 training steps: ~84% **Coding Pass Rate – Proposed (GRPO) (Light Green Line):** The light green line shows a significant upward trend, starting at approximately 58% at 0 training steps. It increases rapidly and then begins to level off. * At 25 training steps: ~64% * At 50 training steps: ~72% * At 75 training steps: ~78% * At 100 training steps: ~82% * At 125 training steps: ~85% * At 150 training steps: ~87% * At 200 training steps: ~88% **Coding Pass Rate – AutoGen Team (Light Blue Line):** The light blue line shows an upward trend, but it is less pronounced than the light green line. It starts at approximately 55% at 0 training steps. * At 25 training steps: ~59% * At 50 training steps: ~65% * At 75 training steps: ~70% * At 100 training steps: ~74% * At 125 training steps: ~76% * At 150 training steps: ~78% * At 200 training steps: ~80% ### Key Observations * The "Proposed (GRPO)" method consistently outperforms the "AutoGen Team" method for both "Writing Quality" and "Coding Pass Rate". * The "Coding Pass Rate" shows a larger performance gap between the two methods than "Writing Quality". * Both metrics exhibit diminishing returns with increasing training steps, indicating that the performance gains start to plateau after a certain point. * The "Proposed (GRPO)" method reaches a higher plateau for both metrics. ### Interpretation The data suggests that the "Proposed (GRPO)" method is more effective than the "AutoGen Team" method in both writing quality and coding pass rate. The larger performance difference in coding pass rate indicates that the "Proposed (GRPO)" method may be particularly beneficial for tasks requiring higher accuracy and reliability. The diminishing returns observed with increasing training steps suggest that there is an optimal training duration beyond which further training does not significantly improve performance. This could be due to the model reaching its capacity or the training data becoming saturated. The chart provides a quantitative comparison of the two methods, allowing for informed decision-making regarding which method to employ based on the specific requirements of the task. The consistent outperformance of the "Proposed (GRPO)" method suggests a superior underlying algorithm or training strategy.

\n ## Line Chart: Performance Comparison of GRPO vs. AutoGen Team ### Overview The image displays a line chart comparing the performance of two methods, "Proposed (GRPO)" and "AutoGen Team," across two different metrics over the course of training. The chart plots "Score / Pass Rate (%)" against "Training Steps (×10^3)." It demonstrates how both methods improve with increased training, with the GRPO method consistently outperforming the AutoGen Team method on both measured tasks. ### Components/Axes * **Chart Type:** Multi-line chart. * **X-Axis (Horizontal):** * **Label:** `Training Steps (×10^3)` * **Scale:** Linear, from 0 to 200 (representing 0 to 200,000 training steps). * **Major Tick Marks:** 0, 25, 50, 75, 100, 125, 150, 175, 200. * **Y-Axis (Vertical):** * **Label:** `Score / Pass Rate (%)` * **Scale:** Linear, from 50 to 90+. * **Major Tick Marks:** 50, 60, 70, 80, 90. * **Legend:** * **Position:** Bottom-right corner of the chart area. * **Entries (from top to bottom in legend box):** 1. `Writing Quality — Proposed (GRPO)` (Orange line) 2. `Writing Quality — AutoGen Team` (Blue line) 3. `Coding Pass Rate — Proposed (GRPO)` (Green line) 4. `Coding Pass Rate — AutoGen Team` (Yellow line) ### Detailed Analysis The chart contains four distinct data series, each represented by a colored line. The trend for all lines is upward, indicating improvement with more training steps. **1. Writing Quality — Proposed (GRPO) [Orange Line]** * **Trend:** Steep, steady upward slope that begins to plateau slightly after 100,000 steps. * **Key Data Points (Approximate):** * At 0 steps: ~80% * At 50,000 steps: ~88% * At 100,000 steps: ~92% * At 200,000 steps: ~95% **2. Writing Quality — AutoGen Team [Blue Line]** * **Trend:** Steady upward slope, consistently below the GRPO writing quality line. * **Key Data Points (Approximate):** * At 0 steps: ~78% * At 50,000 steps: ~84% * At 100,000 steps: ~87% * At 200,000 steps: ~89% **3. Coding Pass Rate — Proposed (GRPO) [Green Line]** * **Trend:** Steep initial upward slope that gradually becomes less steep but continues to rise. * **Key Data Points (Approximate):** * At 0 steps: ~55% * At 50,000 steps: ~68% * At 100,000 steps: ~73% * At 200,000 steps: ~76% **4. Coding Pass Rate — AutoGen Team [Yellow Line]** * **Trend:** The shallowest upward slope of all four lines, showing the slowest rate of improvement. * **Key Data Points (Approximate):** * At 0 steps: ~52% * At 50,000 steps: ~58% * At 100,000 steps: ~62% * At 200,000 steps: ~65% ### Key Observations 1. **Performance Hierarchy:** For both metrics (Writing Quality and Coding Pass Rate), the "Proposed (GRPO)" method achieves a higher score/pass rate than the "AutoGen Team" method at every measured training step. 2. **Metric Comparison:** Both methods score significantly higher on "Writing Quality" than on "Coding Pass Rate" throughout the training process. The gap between the two metrics is larger for the AutoGen Team method. 3. **Convergence:** The performance gap between the two methods is wider for "Coding Pass Rate" than for "Writing Quality." The GRPO method shows a more dramatic improvement in coding, starting only slightly above the AutoGen Team but finishing with a ~11 percentage point lead. 4. **Diminishing Returns:** All curves show signs of diminishing returns, where the rate of improvement slows as training steps increase. This is most pronounced in the "Writing Quality — Proposed (GRPO)" line after 100,000 steps. ### Interpretation This chart provides strong evidence that the proposed GRPO method is more effective than the AutoGen Team baseline for the tasks of writing quality assessment and coding pass rate evaluation. The data suggests that GRPO not only starts at a higher performance level but also learns more efficiently, as indicated by its steeper learning curves, particularly in the coding domain. The consistent superiority across both metrics implies that the advantages of GRPO are robust and not task-specific. The fact that coding performance starts lower but improves more dramatically for GRPO could indicate that the method is particularly adept at learning complex, structured tasks like code generation or evaluation with sufficient training. The chart effectively communicates that investing in more training steps yields better results for both methods, but the return on investment (in terms of performance gain per step) is higher for the proposed GRPO approach.

## Line Graph: Performance Metrics Over Training Steps ### Overview The image is a line graph comparing the performance of two methods ("Proposed (GRPO)" and "AutoGen Team") across two metrics: "Writing Quality" and "Coding Pass Rate" as training steps increase. The x-axis represents training steps (scaled by 1,000), and the y-axis represents scores/pass rates in percentage. Four lines are plotted, with distinct colors for each metric-method combination. ### Components/Axes - **X-axis**: "Training Steps (x10³)" with markers at 0, 25, 50, 75, 100, 125, 150, 175, and 200. - **Y-axis**: "Score / Pass Rate (%)" with markers at 50, 60, 70, 80, 90. - **Legend**: Located in the bottom-right corner, with four entries: 1. **Orange**: Writing Quality — Proposed (GRPO) 2. **Blue**: Writing Quality — AutoGen Team 3. **Green**: Coding Pass Rate — Proposed (GRPO) 4. **Yellow**: Coding Pass Rate — AutoGen Team ### Detailed Analysis 1. **Writing Quality — Proposed (GRPO)** (Orange): - Starts at ~80% at 0 steps. - Rises sharply to ~95% by 200 steps. - Slope: Steep upward trend, indicating rapid improvement. 2. **Writing Quality — AutoGen Team** (Blue): - Starts at ~78% at 0 steps. - Increases gradually to ~88% by 200 steps. - Slope: Gentle upward trend, slower improvement than GRPO. 3. **Coding Pass Rate — Proposed (GRPO)** (Green): - Starts at ~55% at 0 steps. - Rises steadily to ~75% by 200 steps. - Slope: Moderate upward trend, outperforming AutoGen Team. 4. **Coding Pass Rate — AutoGen Team** (Yellow): - Starts at ~52% at 0 steps. - Increases slowly to ~64% by 200 steps. - Slope: Very gradual upward trend, minimal improvement. ### Key Observations - **Performance Gaps**: - Proposed (GRPO) consistently outperforms AutoGen Team in both metrics. - Largest gap in "Writing Quality" (~7% at 200 steps). - Smaller gap in "Coding Pass Rate" (~11% at 200 steps). - **Trend Acceleration**: - GRPO lines show steeper slopes, suggesting faster learning or optimization. - AutoGen Team lines plateau earlier, indicating diminishing returns. ### Interpretation The data demonstrates that the **Proposed (GRPO)** method significantly outperforms the **AutoGen Team** in both writing quality and coding pass rates. The steeper slopes of GRPO lines suggest it achieves higher efficiency in training, likely due to better optimization or architectural advantages. The AutoGen Team's gradual improvement implies reliance on slower, incremental learning. The smaller gap in coding pass rates may reflect inherent differences in task complexity or evaluation criteria. These results highlight GRPO's potential as a superior approach for tasks requiring rapid performance gains.