## Line Chart: Pass@k (%) Performance Across Models ### Overview This image is a line chart comparing the performance of four different machine learning models or training methodologies across a metric denoted as "Pass@k (%)". The x-axis represents the value of 'k' (1, 2, and 3), and the y-axis represents the percentage score. ### Components/Axes * **Y-axis (Left, Vertical):** * **Label:** `Pass@k (%)` * **Scale:** Ranges from 5.0 to 17.5. * **Major Tick Markers:** 5.0, 7.5, 10.0, 12.5, 15.0, 17.5. * **X-axis (Bottom, Horizontal):** * **Label:** `k` * **Scale:** Discrete integer values. * **Major Tick Markers:** 1, 2, 3. * **Legend (Bottom-Right):** Positioned inside the chart area, bounded by a rounded rectangle. It maps colors to model names: * Red line with circle: `RL` * Orange line with circle: `SFT` * Purple line with circle: `MT` * Blue line with circle: `Base` * **Grid:** Light gray, dashed grid lines align with both the x-axis and y-axis major tick marks. ### Detailed Analysis *Component Isolation: Main Chart Area* **Trend Verification & Spatial Grounding:** Before extracting data points, I will verify the visual trend of each line and cross-reference the color with the legend located in the bottom-right. Note: A distinct visual feature is that all data points at `k=1` are marked with an 'x', whereas data points at `k=2` and `k=3` are marked with solid circles. 1. **MT Series (Purple Line):** * *Trend:* The purple line occupies the highest position on the chart. It slopes upward consistently from left to right. * *Data Points (Approximate, ±0.2%):* * k=1 ('x' marker): ~12.7% (Slightly above the 12.5 grid line) * k=2 (circle marker): ~15.3% (Slightly above the 15.0 grid line) * k=3 (circle marker): ~16.7% (Between 15.0 and 17.5, closer to 17.5) 2. **SFT Series (Orange Line):** * *Trend:* The orange line is positioned second from the top. It slopes upward consistently, running roughly parallel to the MT line. * *Data Points (Approximate, ±0.2%):* * k=1 ('x' marker): ~12.0% (Below the 12.5 grid line) * k=2 (circle marker): ~14.0% (Slightly below the midpoint of 12.5 and 15.0) * k=3 (circle marker): ~15.7% (Above the 15.0 grid line) 3. **RL Series (Red Line):** * *Trend:* The red line is positioned third from the top. It slopes upward consistently, running roughly parallel to the SFT line. * *Data Points (Approximate, ±0.2%):* * k=1 ('x' marker): ~11.7% (Below the SFT point, above 10.0) * k=2 (circle marker): ~13.3% (Above the 12.5 grid line) * k=3 (circle marker): ~14.3% (Below the 15.0 grid line) 4. **Base Series (Blue Line):** * *Trend:* The blue line occupies the lowest position on the chart. It exhibits a sharp upward slope from k=1 to k=2, but then flattens out significantly, showing very little growth between k=2 and k=3. * *Data Points (Approximate, ±0.2%):* * k=1 ('x' marker): ~8.7% (Between 7.5 and 10.0) * k=2 (circle marker): ~12.0% (Below the 12.5 grid line) * k=3 (circle marker): ~12.3% (Barely above the k=2 point) ### Key Observations * **Strict Hierarchy:** There is no crossover between any of the lines. The performance ranking is strictly maintained across all values of k: MT > SFT > RL > Base. * **Diminishing Returns for Base:** The `Base` model shows a distinct anomaly compared to the others. While RL, SFT, and MT continue to improve at a relatively steady rate from k=2 to k=3, the `Base` model's performance plateaus. * **Marker Anomaly:** The use of 'x' markers exclusively at `k=1` and solid circles at `k=2` and `k=3` suggests a methodological difference in how the metric is calculated or how the models generate outputs at k=1 versus higher values of k. ### Interpretation The chart demonstrates the effectiveness of different training paradigms on a generative task (likely code generation or mathematical reasoning, where "Pass@k" is a standard metric measuring the probability that at least one out of 'k' generated samples is correct). The data clearly shows that the `Base` model is the weakest performer. Applying Reinforcement Learning (`RL`) provides a noticeable boost. Supervised Fine-Tuning (`SFT`) provides an even larger boost. The method labeled `MT` (which likely stands for Multi-Task training or a similar advanced paradigm) yields the highest overall performance. **Reading between the lines:** The plateau of the `Base` model between k=2 and k=3 is highly informative. It suggests that the `Base` model lacks diversity in its generated outputs; if it doesn't get the correct answer within the first two attempts, generating a third attempt is highly unlikely to yield a correct result. Conversely, the fine-tuned models (`RL`, `SFT`, `MT`) continue to benefit from higher 'k' values. This indicates that these advanced training methods not only improve top-1 accuracy but also create a "deeper" and more diverse distribution of correct answers, making it worthwhile to sample more heavily from them. The change in marker shape at k=1 ('x') likely denotes that k=1 uses a greedy decoding strategy (picking the single most likely output), whereas k=2 and k=3 utilize a sampling strategy (like temperature sampling) to generate multiple distinct outputs.