## Line Chart: Pass@k Performance Comparison ### Overview The image is a line chart comparing the performance of four different models (RL, SFT, MT, and Base) based on the "Pass@k" metric. The x-axis represents the value of 'k' (1, 2, and 3), and the y-axis represents the Pass@k score in percentage. The chart displays how the Pass@k score changes for each model as 'k' increases. ### Components/Axes * **X-axis:** * Label: "k" * Scale: Categorical values 1, 2, and 3. * **Y-axis:** * Label: "Pass@k (%)" * Scale: Ranges from 5 to 35, with tick marks at intervals of 5 (5, 10, 15, 20, 25, 30, 35). * **Legend:** Located on the right side of the chart. * RL (Red line) * SFT (Orange line) * MT (Purple line) * Base (Blue line) ### Detailed Analysis * **RL (Red):** The red line represents the RL model. * k=1: Pass@k ≈ 17% * k=2: Pass@k ≈ 27% * k=3: Pass@k ≈ 31% * Trend: The Pass@k score increases as 'k' increases. * **SFT (Orange):** The orange line represents the SFT model. * k=1: Pass@k ≈ 17% * k=2: Pass@k ≈ 26% * k=3: Pass@k ≈ 34% * Trend: The Pass@k score increases as 'k' increases. * **MT (Purple):** The purple line represents the MT model. * k=1: Pass@k ≈ 16% * k=2: Pass@k ≈ 25% * k=3: Pass@k ≈ 30% * Trend: The Pass@k score increases as 'k' increases. * **Base (Blue):** The blue line represents the Base model. * k=1: Pass@k ≈ 18% * k=2: Pass@k ≈ 26% * k=3: Pass@k ≈ 30% * Trend: The Pass@k score increases as 'k' increases. ### Key Observations * All four models show an increase in Pass@k score as 'k' increases from 1 to 3. * The SFT model (orange line) generally has the highest Pass@k score across all values of 'k'. * The MT model (purple line) generally has the lowest Pass@k score across all values of 'k'. * The Base model (blue line) and RL model (red line) perform similarly. ### Interpretation The chart demonstrates the performance of different models (RL, SFT, MT, and Base) in terms of Pass@k. The increasing trend of all lines indicates that as 'k' increases, the models are more likely to pass the evaluation criteria. The SFT model consistently outperforms the other models, suggesting it is the most effective among the four in this context. The MT model shows the weakest performance. The Base and RL models are comparable. The data suggests that increasing 'k' generally improves the Pass@k score for all models, but the extent of improvement varies depending on the model architecture or training method.

## Line Chart: Pass@k Performance Comparison ### Overview This image is a line chart comparing the performance of four different models or methodologies across three discrete evaluation steps. The metric being measured is "Pass@k (%)" on the y-axis against the variable "k" on the x-axis. All text in the image is in English. ### Components/Axes **Spatial Layout & Regions:** * **Main Chart Area:** Occupies the majority of the image, featuring a white background with a light gray, dashed grid. * **Y-axis (Left):** Vertical axis labeled **"Pass@k (%)"**. The text is oriented vertically, reading from bottom to top. The scale ranges from 5 to 35, with major tick marks and horizontal grid lines at intervals of 5 (5, 10, 15, 20, 25, 30, 35). * **X-axis (Bottom):** Horizontal axis labeled **"k"**. The scale consists of three discrete, evenly spaced points marked as 1, 2, and 3. Vertical grid lines align with these points. * **Legend (Bottom-Right):** Located inside the chart area in the lower right quadrant. It is enclosed in a white box with a thin, light gray border. It defines four data series using line color and a circular marker: * Red line with circle: **RL** * Orange line with circle: **SFT** * Purple line with circle: **MT** * Blue line with circle: **Base** *Note on Markers:* While the legend displays circular markers for all series, the actual plotted data points on the chart use an 'x' marker at k=1, and circular markers at k=2 and k=3. ### Detailed Analysis **Trend Verification & Data Extraction:** All four data series exhibit a positive, upward trend, indicating that the Pass@k percentage increases as 'k' increases from 1 to 3. *Values below are visually interpolated and approximate (±0.5%).* 1. **Base (Blue Line)** * *Trend:* Slopes upward moderately from k=1 to k=2, and continues upward at a slightly shallower angle from k=2 to k=3. * **k=1:** ~18.5% (Highest starting value) * **k=2:** ~26.0% * **k=3:** ~30.8% 2. **RL (Red Line)** * *Trend:* Slopes upward steeply from k=1 to k=2 (crossing above the Base line), then the slope flattens slightly from k=2 to k=3. * **k=1:** ~16.8% * **k=2:** ~27.5% (Highest value at k=2) * **k=3:** ~31.0% 3. **SFT (Orange Line)** * *Trend:* Slopes upward from k=1 to k=2, and then exhibits the steepest upward slope of any line from k=2 to k=3, crossing above both Base and RL. * **k=1:** ~16.5% * **k=2:** ~26.8% * **k=3:** ~34.5% (Highest ending value) 4. **MT (Purple Line)** * *Trend:* Slopes upward consistently from k=1 to k=3. It remains the lowest performing series across all values of k. * **k=1:** ~15.8% (Lowest starting value) * **k=2:** ~25.5% * **k=3:** ~29.8% (Lowest ending value) **Reconstructed Data Table:** | k | Base (Blue) | RL (Red) | SFT (Orange) | MT (Purple) | |---|---|---|---|---| | **1** | ~18.5% | ~16.8% | ~16.5% | ~15.8% | | **2** | ~26.0% | ~27.5% | ~26.8% | ~25.5% | | **3** | ~30.8% | ~31.0% | ~34.5% | ~29.8% | ### Key Observations * **Crossovers:** The "Base" model starts with the highest performance at k=1 but is overtaken by "RL" and "SFT" at k=2, and remains below them at k=3. * **Late Surge:** The "SFT" model shows a significant acceleration in performance between k=2 and k=3, separating itself from the cluster to achieve the highest overall score. * **Consistent Underperformer:** The "MT" model consistently scores the lowest across all three measured points, though its rate of improvement (slope) is roughly parallel to the "Base" model. * **Marker Anomaly:** The use of 'x' markers exclusively at k=1 suggests a potential difference in how the k=1 metric was calculated or represents a baseline state compared to k=2 and k=3 (which use circles). ### Interpretation In the context of machine learning (specifically generative AI or code generation), "Pass@k" measures the probability that at least one out of 'k' generated samples passes a specific test or criteria. * **Baseline vs. Fine-tuning:** The data suggests that the "Base" model is relatively strong at generating a correct answer on the very first try (k=1). However, the fine-tuned models (RL - Reinforcement Learning, and SFT - Supervised Fine-Tuning) benefit much more from being allowed multiple attempts (k=2, k=3). * **Diversity of Output:** The steep rise of the SFT and RL curves implies these methods produce a higher diversity of plausible answers. If the first answer is wrong, subsequent generated answers are highly likely to be different and correct, pushing their Pass@2 and Pass@3 scores higher than the Base model. The Base model might be generating similar (incorrect) variations of its first attempt, leading to a flatter curve. * **SFT Efficacy at Scale:** The SFT method scales the best with multiple attempts, suggesting it has learned a wide distribution of correct patterns that are revealed when given a larger budget of generations (k=3).

\n ## Line Chart: Pass@k vs. k for Different Models ### Overview This image presents a line chart comparing the Pass@k metric for four different models (RL, SFT, MT, and Base) across varying values of 'k' (1, 2, and 3). The chart visualizes how the percentage of successful passes changes as the value of 'k' increases for each model. ### Components/Axes * **X-axis:** Labeled "k", with values 1, 2, and 3. * **Y-axis:** Labeled "Pass@k (%)", with a scale ranging from 5% to 35%. * **Legend:** Located in the top-right corner, identifying the four data series: * RL (Red) * SFT (Orange) * MT (Purple) * Base (Blue) * **Gridlines:** Present to aid in reading values. ### Detailed Analysis Let's analyze each line and extract the approximate data points. * **RL (Red):** The line slopes upward, indicating an increase in Pass@k as k increases. * k = 1: Approximately 17% * k = 2: Approximately 27% * k = 3: Approximately 31% * **SFT (Orange):** The line shows a significant increase from k=1 to k=2, then a smaller increase from k=2 to k=3. * k = 1: Approximately 19% * k = 2: Approximately 27% * k = 3: Approximately 34% * **MT (Purple):** The line slopes upward, but less steeply than RL and SFT. * k = 1: Approximately 16% * k = 2: Approximately 26% * k = 3: Approximately 30% * **Base (Blue):** The line shows a moderate increase from k=1 to k=2, and a smaller increase from k=2 to k=3. * k = 1: Approximately 15% * k = 2: Approximately 23% * k = 3: Approximately 29% ### Key Observations * The SFT model consistently achieves the highest Pass@k values across all 'k' values. * The RL and MT models perform similarly, with RL slightly outperforming MT at k=3. * The Base model consistently has the lowest Pass@k values. * All models show an improvement in Pass@k as 'k' increases, but the rate of improvement varies. ### Interpretation The chart demonstrates the impact of different model training approaches (RL, SFT, MT, and Base) on the Pass@k metric, which likely represents the success rate of a model in passing a certain test or benchmark. The 'k' parameter likely represents the number of attempts or samples considered. The superior performance of the SFT model suggests that supervised fine-tuning is an effective strategy for improving the model's ability to pass the test. The increasing Pass@k values with increasing 'k' indicate that allowing the model more attempts or considering more samples improves its chances of success. The differences between the models suggest that the training data and methods used have a significant impact on performance. The Base model's lower performance may indicate that it lacks the specific knowledge or skills required to succeed on the test. The relatively small difference between k=2 and k=3 for all models suggests diminishing returns as 'k' increases, indicating that there may be a point where additional attempts or samples do not significantly improve performance.

\n ## Line Chart: Pass@k Performance Comparison ### Overview This image displays a line chart comparing the performance of four different models or methods (RL, SFT, MT, Base) on a metric called "Pass@k" across three discrete values of k (1, 2, and 3). The chart shows a clear upward trend for all methods as k increases. ### Components/Axes * **Chart Type:** Line chart with markers. * **X-Axis:** * **Label:** `k` * **Scale:** Discrete, linear scale with major ticks at `1`, `2`, and `3`. * **Y-Axis:** * **Label:** `Pass@k (%)` * **Scale:** Linear scale ranging from 5 to 35, with major ticks every 5 units (5, 10, 15, 20, 25, 30, 35). * **Legend:** * **Position:** Bottom-right corner of the plot area. * **Entries (from top to bottom):** 1. **RL:** Red line with circular markers. 2. **SFT:** Orange line with circular markers. 3. **MT:** Purple line with circular markers. 4. **Base:** Blue line with circular markers (note: at k=1, the marker is an 'x' instead of a circle). * **Grid:** Light gray dashed grid lines are present for both axes. ### Detailed Analysis **Trend Verification:** All four data series exhibit a positive, upward-sloping trend from k=1 to k=3. The slope appears steepest between k=1 and k=2 for all series. **Data Points (Approximate Values):** | Method (Color) | k=1 | k=2 | k=3 | | :--- | :--- | :--- | :--- | | **RL (Red)** | ~17% | ~27.5% | ~31% | | **SFT (Orange)** | ~16.5% | ~26.5% | ~34.5% | | **MT (Purple)** | ~15.5% | ~25.5% | ~29.5% | | **Base (Blue)** | ~18.5% | ~26% | ~30.5% | **Spatial Grounding & Component Isolation:** * **Header/Title:** No chart title is present. * **Main Chart Area:** Contains the four plotted lines and the grid. * **Footer/Axes:** The x-axis label "k" is centered below the axis. The y-axis label "Pass@k (%)" is rotated 90 degrees and placed to the left of the axis. * **Legend:** Located in the bottom-right quadrant, overlapping slightly with the grid lines but not obscuring data points. ### Key Observations 1. **Performance Hierarchy at k=1:** The `Base` model (blue) starts with the highest Pass@1 score (~18.5%), followed by `RL` (~17%), `SFT` (~16.5%), and `MT` (~15.5%). 2. **Performance Hierarchy at k=3:** The order changes significantly. `SFT` (orange) achieves the highest Pass@3 score (~34.5%), followed by `RL` (~31%), `Base` (~30.5%), and `MT` (~29.5%). 3. **Rate of Improvement:** The `SFT` method shows the most dramatic improvement, increasing by approximately 18 percentage points from k=1 to k=3. Its slope is the steepest, especially between k=2 and k=3. 4. **Crossover Point:** Between k=1 and k=2, the `RL` (red) line crosses above the `Base` (blue) line. The `SFT` (orange) line also crosses above the `Base` line in this interval. 5. **Marker Anomaly:** The `Base` series uses a distinct 'x' marker at k=1, while all other data points across all series use circular markers. ### Interpretation This chart likely evaluates the effectiveness of different training or decoding strategies (Reinforcement Learning - RL, Supervised Fine-Tuning - SFT, perhaps Multi-Task - MT) against a baseline model (Base) on a code generation or problem-solving task, where "Pass@k" measures the probability that at least one of k generated samples is correct. The data suggests that while the `Base` model is the strongest for single-attempt generation (k=1), the specialized training methods (`SFT` and `RL`) scale better with increased sampling (higher k). `SFT` demonstrates the most significant benefit from additional attempts, ultimately outperforming all other methods at k=3. This implies that the `SFT` method produces a more diverse set of high-quality candidate solutions, increasing the likelihood of finding a correct one when given multiple chances. The `MT` method, while improving, consistently underperforms the other approaches across all k values shown. The crossover between `RL`/`SFT` and `Base` highlights a key trade-off: the baseline may be better for efficiency (single try), but the fine-tuned methods are superior when computational resources allow for multiple sampling attempts.

# Technical Document Extraction: Line Chart Analysis ## 1. Axis Labels and Markers - **X-axis**: Labeled "k" with discrete ticks at positions 1, 2, 3. - **Y-axis**: Labeled "Pass@k (%)" with a range from 5% to 35%, incremented by 5%. - **Grid**: Dashed gray gridlines for reference. ## 2. Legend and Color Mapping - **Legend Position**: Lower-right quadrant of the chart. - **Color-Label Mapping**: - **Red**: RL (Reinforcement Learning) - **Orange**: SFT (Softmax Fine-Tuning) - **Purple**: MT (Model Tuning) - **Blue**: Base (Baseline) ## 3. Data Series and Trends ### RL (Red Line) - **Trend**: Steady upward slope with moderate curvature. - **Data Points**: - k=1: ~17% - k=2: ~27% - k=3: ~31% ### SFT (Orange Line) - **Trend**: Sharpest upward trajectory, most aggressive growth. - **Data Points**: - k=1: ~16% - k=2: ~26% - k=3: ~34.5% ### MT (Purple Line) - **Trend**: Consistent linear increase, least curvature. - **Data Points**: - k=1: ~15.5% - k=2: ~25.5% - k=3: ~30% ### Base (Blue Line) - **Trend**: Slightly curved upward, closely follows RL. - **Data Points**: - k=1: ~18.5% - k=2: ~26% - k=3: ~30.5% ## 4. Spatial Grounding - **Legend**: Located at [x=0.85, y=0.15] (normalized coordinates). - **Data Point Verification**: - All line colors match legend labels (e.g., red = RL, orange = SFT). ## 5. Key Observations - **Performance Gaps**: - At k=3, SFT outperforms all methods by ~4.5% over RL and ~4% over Base. - MT consistently lags by ~1–2% compared to RL/Base. - **Scalability**: All methods improve with increasing k, but SFT shows the highest scalability. ## 6. Missing Elements - No embedded text, tables, or non-English content detected. - No heatmap or multi-category sub-categories present. ## 7. Final Data Table Reconstruction | k | RL (%) | SFT (%) | MT (%) | Base (%) | |----|--------|---------|--------|----------| | 1 | 17 | 16 | 15.5 | 18.5 | | 2 | 27 | 26 | 25.5 | 26 | | 3 | 31 | 34.5 | 30 | 30.5 |