## Line Chart: R1-Llama | GK23EN ### Overview The image is a line chart comparing the accuracy (%) of different models (Full, Random, Bottom, Top) against the ratio (%) on the x-axis. The chart shows how the accuracy changes as the ratio increases for each model. ### Components/Axes * **Title:** R1-Llama | GK23EN * **X-axis:** Ratio (%) with markers at 2, 4, 6, 8, 10, 20, 30, 40, 50 * **Y-axis:** Accuracy (%) with markers at 62, 64, 66, 68, 70, 72, 74 * **Legend:** Located at the top-right of the chart. * Full (gray dashed line with x markers) * Random (green line with triangle markers) * Bottom (blue line with square markers) * Top (red line with circle markers) ### Detailed Analysis * **Full:** The gray dashed line with 'x' markers remains relatively constant at approximately 72.7% accuracy across all ratios. * **Random:** The green line with triangle markers shows an increasing trend. * At Ratio 2%, accuracy is approximately 62%. * At Ratio 50%, accuracy is approximately 70%. * **Bottom:** The blue line with square markers shows an increasing trend. * At Ratio 2%, accuracy is approximately 62%. * At Ratio 50%, accuracy is approximately 67.5%. * **Top:** The red line with circle markers starts high and plateaus. * At Ratio 2%, accuracy is approximately 71.5%. * It increases to approximately 73% around Ratio 6%. * It remains relatively constant around 73% until Ratio 50%. ### Key Observations * The "Full" model has a consistent accuracy across all ratios. * The "Top" model has the highest accuracy overall. * The "Random" and "Bottom" models show increasing accuracy as the ratio increases. ### Interpretation The chart compares the performance of different models (Full, Random, Bottom, Top) in terms of accuracy as the ratio changes. The "Full" model serves as a baseline, maintaining a stable accuracy. The "Top" model consistently outperforms the others. The "Random" and "Bottom" models improve in accuracy as the ratio increases, suggesting that their performance is influenced by the ratio parameter. The "Top" model's high initial accuracy and plateau indicate that it might be less sensitive to changes in the ratio compared to the "Random" and "Bottom" models.

## Line Chart: R1-Llama | GK23EN Accuracy vs. Ratio ### Overview This line chart displays the accuracy of different sampling methods (Full, Bottom, Random, Top) for the R1-Llama model (GK23EN) as a function of the ratio of data used. The x-axis represents the ratio, expressed as a percentage, and the y-axis represents the accuracy, also expressed as a percentage. ### Components/Axes * **Title:** R1-Llama | GK23EN * **X-axis Label:** Ratio (%) * **Y-axis Label:** Accuracy (%) * **Legend:** * Full (represented by a dashed grey line with 'x' markers) * Bottom (represented by a solid blue line with square markers) * Random (represented by a solid green line with triangle markers) * Top (represented by a solid red line with circle markers) * **X-axis Markers:** 2, 4, 6, 8, 10, 20, 30, 40, 50 * **Y-axis Scale:** Ranges from approximately 62% to 74%. ### Detailed Analysis Here's a breakdown of each data series and their trends: * **Top (Red Line):** This line generally slopes upward from 2% to 30% ratio, then plateaus and slightly declines. * Ratio 2%: Accuracy ≈ 72.4% * Ratio 4%: Accuracy ≈ 73.0% * Ratio 6%: Accuracy ≈ 73.4% * Ratio 8%: Accuracy ≈ 73.6% * Ratio 10%: Accuracy ≈ 73.8% * Ratio 20%: Accuracy ≈ 74.0% * Ratio 30%: Accuracy ≈ 74.2% * Ratio 40%: Accuracy ≈ 73.4% * Ratio 50%: Accuracy ≈ 73.8% * **Bottom (Blue Line):** This line shows a consistent upward trend throughout the entire range. * Ratio 2%: Accuracy ≈ 62.8% * Ratio 4%: Accuracy ≈ 63.6% * Ratio 6%: Accuracy ≈ 64.0% * Ratio 8%: Accuracy ≈ 64.4% * Ratio 10%: Accuracy ≈ 64.6% * Ratio 20%: Accuracy ≈ 65.2% * Ratio 30%: Accuracy ≈ 65.8% * Ratio 40%: Accuracy ≈ 67.2% * Ratio 50%: Accuracy ≈ 67.8% * **Random (Green Line):** This line exhibits a slow, steady increase up to 30%, then a steep increase from 30% to 50%. * Ratio 2%: Accuracy ≈ 62.4% * Ratio 4%: Accuracy ≈ 63.2% * Ratio 6%: Accuracy ≈ 63.8% * Ratio 8%: Accuracy ≈ 64.2% * Ratio 10%: Accuracy ≈ 64.5% * Ratio 20%: Accuracy ≈ 65.0% * Ratio 30%: Accuracy ≈ 65.6% * Ratio 40%: Accuracy ≈ 68.2% * Ratio 50%: Accuracy ≈ 70.2% * **Full (Grey Dashed Line):** This line remains relatively flat throughout the entire range, fluctuating around 72.5%. * Ratio 2%: Accuracy ≈ 72.6% * Ratio 4%: Accuracy ≈ 72.8% * Ratio 6%: Accuracy ≈ 72.6% * Ratio 8%: Accuracy ≈ 72.4% * Ratio 10%: Accuracy ≈ 72.4% * Ratio 20%: Accuracy ≈ 72.6% * Ratio 30%: Accuracy ≈ 72.4% * Ratio 40%: Accuracy ≈ 72.2% * Ratio 50%: Accuracy ≈ 72.4% ### Key Observations * The "Top" sampling method consistently achieves the highest accuracy, particularly at higher ratios. * The "Bottom" sampling method shows a steady improvement in accuracy as the ratio increases, but remains significantly lower than the "Top" method. * The "Random" sampling method demonstrates a delayed but substantial increase in accuracy at higher ratios (40% and 50%). * The "Full" sampling method maintains a relatively constant accuracy, suggesting it is not significantly affected by the ratio of data used. ### Interpretation The chart suggests that the "Top" sampling method is the most effective for the R1-Llama model (GK23EN) in terms of achieving high accuracy. The "Bottom" method provides a consistent, albeit lower, level of accuracy. The "Random" method's late surge in accuracy indicates that a larger sample size is crucial for its performance. The "Full" method's stability suggests it may be less sensitive to data quantity, potentially due to its comprehensive nature. The differences in performance between the sampling methods likely stem from the distribution of information within the dataset. The "Top" method may be prioritizing the most informative data points, leading to higher accuracy. The "Random" method's improvement with increased ratio suggests it benefits from a larger, more representative sample. The "Full" method's consistent performance could be attributed to its inclusion of all available data, mitigating the impact of sample size. The plateauing of the "Top" method's accuracy at higher ratios suggests a point of diminishing returns, where adding more data does not significantly improve performance. This could indicate that the model has reached its maximum potential with the available data and sampling strategy.

## Line Chart: R1-Llama | GK23EN ### Overview This is a line chart comparing the performance (Accuracy %) of four different data selection strategies ("Full", "Random", "Bottom", "Top") for a model or system named "R1-Llama" on a task or dataset labeled "GK23EN". The chart plots Accuracy against an increasing Ratio (%). ### Components/Axes * **Chart Title:** "R1-Llama | GK23EN" (Top center) * **Y-Axis:** Label: "Accuracy (%)". Scale: Linear, ranging from 62 to 74, with major tick marks at 62, 64, 66, 68, 70, 72, 74. * **X-Axis:** Label: "Ratio (%)". Scale: Appears to be a logarithmic or custom scale with discrete points at 2, 4, 6, 10, 20, 30, 40, 50. * **Legend:** Positioned in the top-right quadrant of the chart area. It defines four data series: * **Full:** Gray line with 'x' markers. * **Random:** Green line with upward-pointing triangle markers. * **Bottom:** Blue line with square markers. * **Top:** Red line with circle markers. ### Detailed Analysis **Data Series Trends and Approximate Values:** 1. **Full (Gray, 'x'):** * **Trend:** Very flat, nearly horizontal line with a slight upward slope. It represents a high, stable baseline. * **Data Points (Approximate):** * Ratio 2%: ~72.2% * Ratio 4%: ~72.3% * Ratio 6%: ~72.4% * Ratio 10%: ~72.5% * Ratio 20%: ~72.7% * Ratio 30%: ~72.8% * Ratio 40%: ~72.9% * Ratio 50%: ~73.0% 2. **Top (Red, circles):** * **Trend:** Consistently upward-sloping line. It starts below the "Full" line but surpasses it around a 10-20% ratio and maintains the highest accuracy thereafter. * **Data Points (Approximate):** * Ratio 2%: ~71.0% * Ratio 4%: ~71.5% * Ratio 6%: ~72.0% * Ratio 10%: ~72.5% (Intersects with "Full" line) * Ratio 20%: ~73.0% * Ratio 30%: ~73.2% * Ratio 40%: ~73.5% * Ratio 50%: ~73.3% (Slight dip from previous point) 3. **Random (Green, triangles):** * **Trend:** Starts low, increases slowly until a ratio of 20%, after which the slope increases dramatically, showing rapid improvement. * **Data Points (Approximate):** * Ratio 2%: ~62.0% * Ratio 4%: ~62.5% * Ratio 6%: ~63.0% * Ratio 10%: ~64.0% * Ratio 20%: ~65.0% * Ratio 30%: ~67.0% * Ratio 40%: ~68.5% * Ratio 50%: ~70.0% 4. **Bottom (Blue, squares):** * **Trend:** Starts at the same low point as "Random", increases slowly and steadily, but at a slower rate than "Random" after the 20% ratio point. It remains the lowest-performing strategy at higher ratios. * **Data Points (Approximate):** * Ratio 2%: ~62.0% * Ratio 4%: ~62.2% * Ratio 6%: ~62.5% * Ratio 10%: ~63.5% * Ratio 20%: ~64.5% * Ratio 30%: ~65.0% * Ratio 40%: ~66.0% * Ratio 50%: ~67.0% ### Key Observations * **Performance Hierarchy:** At low ratios (2-6%), the hierarchy is clear: Full ≈ Top > Random ≈ Bottom. At high ratios (40-50%), the hierarchy is: Top > Full > Random > Bottom. * **Crossover Point:** The "Top" strategy's accuracy line crosses above the "Full" strategy's line between the 10% and 20% ratio marks. * **Divergence of Random/Bottom:** The "Random" and "Bottom" strategies start at identical accuracy (~62%) but begin to diverge significantly after the 10% ratio, with "Random" improving much faster. * **Stability vs. Growth:** The "Full" dataset line is remarkably stable, showing minimal gain from increasing ratio. The "Top" strategy shows consistent growth. The "Random" strategy shows a "hockey stick" growth curve, accelerating after 20%. ### Interpretation This chart demonstrates the impact of data selection quality and quantity on model accuracy for the R1-Llama model on the GK23EN task. * **Quality over Quantity (Initially):** Using a small ratio (2-10%) of the highest-ranked data ("Top") achieves accuracy comparable to using the entire dataset ("Full"), suggesting high data efficiency and that a core subset of high-quality data is highly informative. * **The Power of Curation:** As the data ratio increases, the curated "Top" subset not only matches but eventually surpasses the performance of the full dataset. This implies that the full dataset may contain noise or lower-quality examples that, when included in large proportions, can slightly hinder peak performance compared to a perfectly curated set. * **Random Sampling's Threshold:** The "Random" strategy's poor initial performance and later acceleration suggest a critical mass effect. Below a ~20% ratio, random data lacks sufficient coverage of important patterns. Beyond that threshold, the added diversity begins to pay off rapidly, though it never catches up to curated selection within the tested range. * **The "Bottom" Penalty:** Consistently selecting the lowest-ranked data ("Bottom") yields the worst performance, confirming that the ranking metric used is meaningful and that low-quality data is actively detrimental or, at best, minimally helpful. **In essence, the data argues for intelligent data curation ("Top" selection) as the most effective strategy, offering both high efficiency at low ratios and superior peak performance at higher ratios.** The full dataset is a robust but sub-optimal baseline, while random selection requires a significant volume of data to become competitive.

## Line Chart: R1-Llama | GK23EN Accuracy vs. Ratio ### Overview The chart compares the accuracy performance of four methods ("Full," "Bottom," "Random," "Top") across varying ratios (2% to 50%). Accuracy is measured on the y-axis (62%–74%), while the x-axis represents the ratio percentage. The "Full" method shows the highest baseline accuracy, while "Random" demonstrates the steepest improvement with increasing ratio. ### Components/Axes - **X-axis (Ratio %)**: Increments of 2% from 2% to 50%. - **Y-axis (Accuracy %)**: Range from 62% to 74%. - **Legend**: Positioned on the right, with four entries: - **Full**: Dashed gray line with star markers. - **Bottom**: Solid blue line with square markers. - **Random**: Solid green line with triangle markers. - **Top**: Solid red line with circle markers. ### Detailed Analysis 1. **Full (Gray Stars)**: - Starts at ~72% accuracy at 2% ratio. - Remains relatively flat, peaking at ~73.5% by 50% ratio. - Minimal fluctuation throughout. 2. **Bottom (Blue Squares)**: - Begins at ~62% accuracy at 2% ratio. - Gradual upward trend, reaching ~67.5% at 50% ratio. - Linear progression with no sharp changes. 3. **Random (Green Triangles)**: - Starts at ~62% accuracy at 2% ratio. - Steep upward trajectory, reaching ~69.5% at 50% ratio. - Accelerates significantly between 20% and 50% ratio. 4. **Top (Red Circles)**: - Begins at ~71% accuracy at 2% ratio. - Consistent upward trend, peaking at ~73.5% by 50% ratio. - Slight dip at 40% ratio (~73%) before recovering. ### Key Observations - **Top vs. Full**: The "Top" method closely matches the "Full" method's performance, suggesting comparable effectiveness despite potential differences in implementation. - **Random's Surge**: The "Random" method shows the most dramatic improvement (7.5% gain from 2% to 50% ratio), indicating sensitivity to ratio increases. - **Bottom's Steady Climb**: The "Bottom" method exhibits the slowest growth (5.5% gain), implying limited responsiveness to ratio changes. - **Convergence at 50%**: All methods except "Full" approach ~67–70% accuracy at maximum ratio, suggesting diminishing returns for non-"Full" approaches. ### Interpretation The data highlights trade-offs between method complexity and scalability: - **"Full" Method**: Likely represents a resource-intensive baseline (e.g., full model training) with stable performance, ideal for high-stakes applications. - **"Top" Method**: A near-optimal alternative to "Full," possibly optimized for efficiency without significant accuracy loss. - **"Random" Method**: Demonstrates high potential for improvement with increased ratio, suggesting it may leverage stochastic processes (e.g., random sampling) that benefit from larger datasets. - **"Bottom" Method**: Least responsive to ratio changes, possibly indicating a fundamental limitation in its design (e.g., fixed feature set). The chart underscores the importance of method selection based on resource constraints and performance requirements. While "Full" and "Top" prioritize stability, "Random" offers a cost-effective path to higher accuracy with sufficient ratio investment.