## Line Chart: Pass@k vs. k for Different Models ### 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 for varying values of 'k'. The chart displays the relationship between 'k' (x-axis) and "Pass@k (%)" (y-axis) for each model. ### Components/Axes * **X-axis:** 'k' with values 1, 2, and 3. * **Y-axis:** "Pass@k (%)" ranging from 0 to 20, with tick marks at intervals of 5 (0, 5, 10, 15, 20). * **Legend:** Located in the top-right corner, associating each model with a specific color: * RL: Red * SFT: Orange * MT: Purple * Base: Blue * **Gridlines:** Light gray, dashed gridlines are present in the background. ### Detailed Analysis * **RL (Red):** The red line represents the RL model. It shows an upward trend. * k=1: Pass@k ≈ 8% (marked with an 'x') * k=2: Pass@k ≈ 16% (marked with a circle) * k=3: Pass@k ≈ 21% (marked with a circle) * **SFT (Orange):** The orange line represents the SFT model. It shows an upward trend. * k=1: Pass@k ≈ 6% (marked with an 'x') * k=2: Pass@k ≈ 9% (marked with a circle) * k=3: Pass@k ≈ 13% (marked with a circle) * **MT (Purple):** The purple line represents the MT model. It shows an upward trend. * k=1: Pass@k ≈ 7% (marked with an 'x') * k=2: Pass@k ≈ 12% (marked with a circle) * k=3: Pass@k ≈ 17% (marked with a circle) * **Base (Blue):** The blue line represents the Base model. It shows an upward trend. * k=1: Pass@k ≈ 3% (marked with an 'x') * k=2: Pass@k ≈ 8% (marked with a circle) * k=3: Pass@k ≈ 12% (marked with a circle) ### Key Observations * The RL model consistently outperforms the other models across all values of 'k'. * The Base model consistently shows the lowest performance. * All models exhibit an increase in "Pass@k" as 'k' increases. * The gap between the RL model and the other models appears to widen as 'k' increases. ### Interpretation The chart demonstrates that the RL model has the highest "Pass@k" performance compared to SFT, MT, and Base models for the given values of 'k'. The upward trend for all models suggests that increasing 'k' generally improves the "Pass@k" metric, indicating a higher probability of finding a correct solution within the top 'k' attempts. The RL model's superior performance suggests it is more effective at generating correct solutions compared to the other models. The widening gap between RL and the other models as 'k' increases implies that RL's advantage becomes more pronounced with more attempts allowed.

## Line Chart: Pass@k (%) Performance Across Training Methods ### 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 The image can be isolated into the following distinct components: * **Y-axis (Left):** * **Label:** "Pass@k (%)" (Text is rotated 90 degrees counter-clockwise). * **Scale:** Ranges from 0 to roughly 25, with visible tick marks and corresponding horizontal dashed grid lines at 0, 5, 10, 15, and 20. * **X-axis (Bottom):** * **Label:** "k". * **Scale:** Discrete categorical/numerical markers at 1, 2, and 3. Vertical dashed grid lines align with these markers. * **Legend (Top-Left):** A bounding box containing four entries, mapping line colors to model names. * Red line with a circular marker: "RL" * Orange line with a circular marker: "SFT" * Purple line with a circular marker: "MT" * Blue line with a circular marker: "Base" * **Main Chart Area:** Contains four distinct lines plotting data points across the x-axis grid lines. Notably, the data points at k=1 are marked with an 'x' symbol, while the data points at k=2 and k=3 are marked with solid circles. ### Detailed Analysis **Trend Verification:** Before extracting specific values, a visual inspection of the trends reveals that all four lines slope upward from left to right. This indicates a positive correlation for all models: as 'k' increases, the 'Pass@k (%)' score increases. Furthermore, the lines never intersect; they maintain a strict vertical hierarchy across all values of 'k'. **Data Extraction (Approximate Values):** *Cross-referencing the legend colors with the lines from top to bottom:* 1. **RL (Red Line - Top-most position):** * *Trend:* Slopes upward steeply, showing the highest rate of growth. * k=1 (marked with 'x'): ~8.3% * k=2 (marked with circle): ~16.0% * k=3 (marked with circle): ~20.8% 2. **MT (Purple Line - Second from top):** * *Trend:* Slopes upward steadily, maintaining a consistent gap below the RL line. * k=1 (marked with 'x'): ~7.0% * k=2 (marked with circle): ~12.4% * k=3 (marked with circle): ~17.4% 3. **SFT (Orange Line - Third from top):** * *Trend:* Slopes upward, but at a slightly shallower angle than RL and MT. * k=1 (marked with 'x'): ~5.7% * k=2 (marked with circle): ~9.7% * k=3 (marked with circle): ~13.0% 4. **Base (Blue Line - Bottom-most position):** * *Trend:* Slopes upward, maintaining a relatively parallel trajectory to the SFT line. * k=1 (marked with 'x'): ~3.0% * k=2 (marked with circle): ~8.0% * k=3 (marked with circle): ~12.0% ### Key Observations * **Strict Hierarchy:** The performance ranking is absolute across all measured points: RL > MT > SFT > Base. * **Divergence:** The performance gap between the best method (RL) and the worst method (Base) widens as 'k' increases. At k=1, the gap is roughly 5.3 percentage points. At k=3, the gap expands to roughly 8.8 percentage points. * **Marker Anomaly:** The legend shows only circular markers for all lines. However, on the chart itself, the data points at k=1 are plotted using 'x' markers, while k=2 and k=3 use circles. ### Interpretation This chart is highly characteristic of evaluation metrics used in generative Artificial Intelligence, specifically Large Language Models (LLMs) evaluated on coding or reasoning tasks (like the HumanEval benchmark). * **The Metric:** "Pass@k" measures the probability that at least one out of 'k' generated responses is correct. Naturally, as a model is allowed more attempts (higher 'k'), the probability of getting at least one correct answer increases, which explains the universal upward trend. * **The Models (Reading between the lines):** The labels represent standard stages in modern AI model training pipelines: * **Base:** The foundational, pre-trained model (lowest performance). * **SFT:** Supervised Fine-Tuning. Training the base model on high-quality instruction-response pairs yields a noticeable improvement. * **MT:** Likely stands for Multi-Task training (or potentially a specific intermediate tuning phase), showing further improvement over standard SFT. * **RL:** Reinforcement Learning (often RLHF - Reinforcement Learning from Human Feedback or RLAIF). This technique yields the highest performance. * **Significance:** The data demonstrates the compounding value of advanced alignment techniques. Not only does RL have the highest baseline accuracy (Pass@1), but its steeper slope indicates it benefits more from multiple sampling attempts (Pass@2, Pass@3) than the Base or SFT models. This suggests the RL model generates a higher diversity of viable, correct answers when sampled multiple times. * **The 'x' Marker:** The distinct 'x' marker at k=1 likely denotes a methodological difference in how the data was gathered. Pass@1 is often evaluated using "greedy decoding" (temperature = 0, picking the single most likely token), whereas Pass@k (where k > 1) requires sampling with a higher temperature to generate diverse responses. The change in marker shape visually separates the deterministic evaluation (k=1) from the probabilistic sampling evaluations (k=2, 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 illustrates how the percentage of successful passes changes as 'k' increases for each model. ### Components/Axes * **X-axis:** Labeled "k", with markers at 1, 2, and 3. * **Y-axis:** Labeled "Pass@k (%)", with a scale ranging from 0 to 20, incrementing by 5. * **Legend:** Located in the top-left corner, listing the models and their corresponding colors: * RL (Red) * SFT (Orange) * MT (Purple) * Base (Blue) * **Data Series:** Four distinct lines representing each model's performance. ### Detailed Analysis Let's analyze each line's trend and extract the approximate data points. * **RL (Red):** The line slopes upward consistently. * k=1: Approximately 7.5% * k=2: Approximately 9% * k=3: Approximately 21% * **SFT (Orange):** The line also slopes upward, but less steeply than RL. * k=1: Approximately 4.5% * k=2: Approximately 8% * k=3: Approximately 12% * **MT (Purple):** The line slopes upward, with a moderate steepness. * k=1: Approximately 7% * k=2: Approximately 13% * k=3: Approximately 18% * **Base (Blue):** The line slopes upward, but is the least steep of all four. * k=1: Approximately 2.5% * k=2: Approximately 6% * k=3: Approximately 12.5% ### Key Observations * The RL model consistently outperforms the other models across all values of 'k'. * The Base model consistently underperforms the other models across all values of 'k'. * All models show an increase in Pass@k as 'k' increases, indicating that allowing more attempts improves performance. * The difference in performance between the models becomes more pronounced as 'k' increases. ### Interpretation The chart demonstrates the impact of different training methodologies (RL, SFT, MT, and Base) on the Pass@k metric, which likely represents the success rate of a model in generating acceptable outputs within 'k' attempts. The RL model's superior performance suggests that reinforcement learning is an effective approach for improving the quality of generated outputs. The increasing trend for all models with higher 'k' values indicates a trade-off between efficiency (fewer attempts) and accuracy (higher success rate). The Base model's lower performance suggests that it may require further refinement or a different training strategy. The data suggests that increasing 'k' is a viable strategy for improving performance, but the optimal value of 'k' likely depends on the specific application and the desired balance between efficiency and accuracy. The gap between the models widens as k increases, suggesting that the benefits of the more advanced training methods are more apparent when more attempts are allowed.

## Line Chart: Pass@k (%) Performance Comparison of Four Methods ### Overview The image is a line chart comparing the performance of four different methods (RL, SFT, MT, Base) on a metric called "Pass@k (%)". The chart plots this metric against three discrete values of `k` (1, 2, and 3). All four methods show a positive, linear trend, with performance improving as `k` increases. ### Components/Axes * **Chart Type:** Line chart with markers. * **X-Axis:** * **Label:** `k` * **Scale/Ticks:** Discrete values at 1, 2, and 3. * **Y-Axis:** * **Label:** `Pass@k (%)` * **Scale/Ticks:** Linear scale from 0 to 20, with major ticks at 0, 5, 10, 15, and 20. * **Legend:** * **Position:** Top-left 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. * **Data Series:** Four distinct lines, each corresponding to a legend entry. Each line connects three data points (at k=1, 2, 3). The data points at k=1 are marked with an 'x' symbol, while points at k=2 and k=3 are marked with filled circles. ### Detailed Analysis **Trend Verification:** All four lines (RL, SFT, MT, Base) slope upward from left to right, indicating that the Pass@k (%) score increases for all methods as `k` increases from 1 to 3. **Data Point Extraction (Approximate Values):** | Method (Legend Color) | k=1 (Pass@k %) | k=2 (Pass@k %) | k=3 (Pass@k %) | | :--- | :--- | :--- | :--- | | **RL (Red)** | ~8.5 | ~16.0 | ~20.5 | | **MT (Purple)** | ~7.0 | ~12.5 | ~17.5 | | **SFT (Orange)** | ~5.5 | ~9.5 | ~13.0 | | **Base (Blue)** | ~3.0 | ~8.0 | ~12.0 | **Component Isolation & Spatial Grounding:** * **Header/Title:** No explicit chart title is present. * **Main Plot Area:** Contains the four data lines and gridlines. * **Axes:** X-axis at the bottom, Y-axis on the left. * **Legend:** Positioned in the upper-left quadrant, overlapping slightly with the gridlines but not obscuring data points. The order in the legend (RL, SFT, MT, Base) corresponds to the vertical order of the lines at k=3 (RL highest, Base lowest). ### Key Observations 1. **Consistent Hierarchy:** The performance ranking of the methods is consistent across all values of `k`. From highest to lowest Pass@k (%): **RL > MT > SFT > Base**. 2. **Linear Improvement:** The relationship between `k` and Pass@k (%) appears approximately linear for all methods within the range shown (k=1 to 3). 3. **Performance Gap:** The absolute performance gap between the top method (RL) and the bottom method (Base) widens as `k` increases. At k=1, the gap is ~5.5 percentage points; at k=3, it is ~8.5 percentage points. 4. **Marker Distinction:** The use of an 'x' marker for the k=1 data point for all series is a notable visual distinction from the circular markers used for k=2 and k=3. ### Interpretation This chart demonstrates the comparative effectiveness of four different training or modeling approaches (likely Reinforcement Learning, Supervised Fine-Tuning, a method labeled MT, and a Baseline) on a task measured by the Pass@k metric. Pass@k is a common metric in code generation and problem-solving tasks, representing the probability that at least one of `k` generated samples is correct. The data suggests that the **RL method is the most effective** of the four, consistently achieving the highest Pass@k scores. The **Base model performs the worst**, indicating that any of the other training methods (SFT, MT, RL) provide a significant improvement over the baseline. The positive slope for all lines indicates that allowing the model to generate more samples (increasing `k`) increases the chance of obtaining a correct solution, which is the expected behavior for the Pass@k metric. The fact that the RL line has the steepest slope suggests its performance benefits the most from an increased sample budget (`k`), or that its top-1 performance (k=1) is particularly strong relative to its top-k performance. The consistent ranking implies a clear hierarchy in the efficacy of these methods for the specific task and evaluation setup used to generate this chart. The MT method occupies a middle ground, outperforming SFT but not reaching the level of RL.

# Technical Document Extraction: Line Chart Analysis ## Chart Overview The image depicts a line chart titled **"Pass@k (%)"** with the following components: ### Axes - **X-axis (Horizontal):** Labeled `k` with discrete values at `1`, `2`, and `3`. - **Y-axis (Vertical):** Labeled `Pass@k (%)` with a range from `0` to `20` in increments of `5`. ### Legend - Located in the **top-left corner** of the chart. - **Color-Coded Labels:** - `RL` (Red) - `SFT` (Orange) - `MT` (Purple) - `Base` (Blue) ### Data Series #### 1. RL (Red Line) - **Trend:** Steep upward slope. - **Data Points:** - `k=1`: `8%` - `k=2`: `16%` - `k=3`: `20.5%` #### 2. SFT (Orange Line) - **Trend:** Moderate upward slope. - **Data Points:** - `k=1`: `5.5%` - `k=2`: `9.5%` - `k=3`: `13%` #### 3. MT (Purple Line) - **Trend:** Steeper than SFT but less than RL. - **Data Points:** - `k=1`: `7%` - `k=2`: `12.5%` - `k=3`: `17.5%` #### 4. Base (Blue Line) - **Trend:** Gentle upward slope. - **Data Points:** - `k=1`: `3%` - `k=2`: `8%` - `k=3`: `12%` ### Key Observations - All lines show **increasing trends** as `k` increases. - `RL` consistently outperforms other methods across all `k` values. - `Base` has the lowest performance, while `RL` achieves the highest Pass@k percentage. ### Spatial Grounding - **Legend Position:** Top-left corner (coordinates: `[x=0, y=0]` relative to chart boundaries). - **Data Point Colors:** Match legend labels exactly (e.g., red for `RL`, orange for `SFT`). ### Trend Verification - **RL:** Sharpest increase (e.g., `8%` → `20.5%` over `k=1` to `k=3`). - **MT:** Second-steepest increase (e.g., `7%` → `17.5%`). - **SFT/Base:** Gradual increases (e.g., `5.5%` → `13%` for SFT; `3%` → `12%` for Base). ### Component Isolation 1. **Header:** Chart title (`Pass@k (%)`) and axis labels. 2. **Main Chart:** Four distinct lines with markers and slopes. 3. **Footer:** No additional text or annotations. ### Final Notes - No non-English text or embedded diagrams present. - All numerical values and labels extracted directly from the chart.