## Bar Chart: Accuracy vs. Difficulty Level for Different Models ### Overview The image is a bar chart comparing the accuracy of three different models (Base Model, SFT Only, and SFT+RL) across five difficulty levels (Very Easy, Easy, Medium, Hard, and Very Hard). The y-axis represents accuracy in percentage, ranging from 0 to 100. The x-axis represents the difficulty level. ### Components/Axes * **Y-axis:** * Label: "Accuracy (%)" * Scale: 0 to 100, with tick marks at intervals of 20 (0, 20, 40, 60, 80, 100). * **X-axis:** * Label: "Difficulty Level" * Categories: 1 (Very Easy), 2 (Easy), 3 (Medium), 4 (Hard), 5 (Very Hard) * **Legend (located at the top-right):** * Base Model (Blue) * SFT Only (Magenta) * SFT+RL (Orange) ### Detailed Analysis Here's a breakdown of the accuracy for each model at each difficulty level: * **Difficulty Level 1 (Very Easy):** * Base Model (Blue): Approximately 86% * SFT Only (Magenta): Approximately 85% * SFT+RL (Orange): Approximately 93% * **Difficulty Level 2 (Easy):** * Base Model (Blue): Approximately 60% * SFT Only (Magenta): Approximately 77% * SFT+RL (Orange): Approximately 83% * **Difficulty Level 3 (Medium):** * Base Model (Blue): Approximately 50% * SFT Only (Magenta): Approximately 71% * SFT+RL (Orange): Approximately 78% * **Difficulty Level 4 (Hard):** * Base Model (Blue): Approximately 40% * SFT Only (Magenta): Approximately 65% * SFT+RL (Orange): Approximately 72% * **Difficulty Level 5 (Very Hard):** * Base Model (Blue): Approximately 20% * SFT Only (Magenta): Approximately 49% * SFT+RL (Orange): Approximately 57% **Trends:** * **Base Model (Blue):** Accuracy decreases significantly as difficulty level increases. * **SFT Only (Magenta):** Accuracy also decreases as difficulty level increases, but generally performs better than the Base Model. * **SFT+RL (Orange):** Accuracy decreases as difficulty level increases, and generally performs the best among the three models. ### Key Observations * The SFT+RL model consistently outperforms the other two models across all difficulty levels. * The Base Model shows the most significant drop in accuracy as difficulty increases. * The difference in accuracy between the models is more pronounced at higher difficulty levels. ### Interpretation The data suggests that both SFT Only and SFT+RL models improve upon the Base Model, with SFT+RL providing the most significant improvement in accuracy, especially as the difficulty of the task increases. This indicates that the SFT+RL model is more robust and better equipped to handle complex tasks compared to the other two models. The steep decline in accuracy for the Base Model as difficulty increases highlights the limitations of the base model in handling more challenging tasks.

\n ## Bar Chart: Accuracy vs. Difficulty Level for Different Models ### Overview This bar chart compares the accuracy of three different models – Base Model, SFT Only, and SFT+RL – across five difficulty levels: (Very Easy), (Easy), (Medium), (Hard), and (Very Hard). Accuracy is measured as a percentage, ranging from 0% to 100%. ### Components/Axes * **X-axis:** Difficulty Level, with categories: 1 (Very Easy), 2 (Easy), 3 (Medium), 4 (Hard), 5 (Very Hard). * **Y-axis:** Accuracy (%), ranging from 0 to 100. * **Legend:** Located in the top-left corner, identifying the three data series: * Base Model (Purple) * SFT Only (Magenta/Pink) * SFT+RL (Orange) ### Detailed Analysis The chart consists of five groups of three bars, one for each model at each difficulty level. **Difficulty Level 1 (Very Easy):** * Base Model: Approximately 84% accuracy. * SFT Only: Approximately 88% accuracy. * SFT+RL: Approximately 94% accuracy. **Difficulty Level 2 (Easy):** * Base Model: Approximately 58% accuracy. * SFT Only: Approximately 76% accuracy. * SFT+RL: Approximately 82% accuracy. **Difficulty Level 3 (Medium):** * Base Model: Approximately 48% accuracy. * SFT Only: Approximately 69% accuracy. * SFT+RL: Approximately 76% accuracy. **Difficulty Level 4 (Hard):** * Base Model: Approximately 40% accuracy. * SFT Only: Approximately 65% accuracy. * SFT+RL: Approximately 68% accuracy. **Difficulty Level 5 (Very Hard):** * Base Model: Approximately 30% accuracy. * SFT Only: Approximately 52% accuracy. * SFT+RL: Approximately 62% accuracy. **Trends:** * **Base Model:** Accuracy decreases consistently as difficulty level increases. * **SFT Only:** Accuracy decreases as difficulty level increases, but at a slower rate than the Base Model. * **SFT+RL:** Accuracy decreases as difficulty level increases, but generally maintains the highest accuracy across all difficulty levels. ### Key Observations * SFT+RL consistently outperforms both the Base Model and SFT Only across all difficulty levels. * The Base Model exhibits the most significant drop in accuracy as difficulty increases. * The difference in accuracy between the models is most pronounced at higher difficulty levels. * All models show a clear negative correlation between difficulty level and accuracy. ### Interpretation The data suggests that incorporating Reinforcement Learning (RL) with Supervised Fine-Tuning (SFT) significantly improves model performance, particularly on more challenging tasks. The Base Model, lacking fine-tuning, struggles with increasing difficulty, indicating the importance of adapting the model to specific task complexities. The SFT Only model shows improvement over the Base Model, demonstrating the benefit of supervised learning. However, the SFT+RL model's consistent superiority highlights the added value of reinforcement learning in optimizing performance. The decreasing accuracy across all models with increasing difficulty is expected, as tasks become inherently more complex and require more sophisticated reasoning and problem-solving capabilities. The gap between the models widens at higher difficulty levels, suggesting that RL is particularly effective in tackling complex challenges. This data supports the idea that a combination of supervised and reinforcement learning techniques is crucial for building robust and adaptable models.

\n ## Grouped Bar Chart: Model Accuracy by Difficulty Level ### Overview The image displays a grouped bar chart comparing the accuracy percentages of three different models across five difficulty levels. The chart visually demonstrates how model performance degrades as task difficulty increases, with one model consistently outperforming the others. ### Components/Axes * **Chart Type:** Grouped Bar Chart. * **X-Axis (Horizontal):** Labeled "Difficulty Level". It contains five categorical groups: 1. `1 (Very Easy)` 2. `2 (Easy)` 3. `3 (Medium)` 4. `4 (Hard)` 5. `5 (Very Hard)` * **Y-Axis (Vertical):** Labeled "Accuracy (%)". The scale runs from 0 to 100 in increments of 20 (0, 20, 40, 60, 80, 100). * **Legend:** Located in the top-right corner of the chart area. It defines three data series: * **Base Model:** Represented by purple bars. * **SFT Only:** Represented by pink/magenta bars. * **SFT+RL:** Represented by orange bars. ### Detailed Analysis The following table reconstructs the approximate accuracy values for each model at each difficulty level. Values are estimated based on bar height relative to the y-axis grid lines. | Difficulty Level | Base Model (Purple) | SFT Only (Pink) | SFT+RL (Orange) | | :--- | :--- | :--- | :--- | | **1 (Very Easy)** | ~85% | ~84% | ~93% | | **2 (Easy)** | ~60% | ~77% | ~83% | | **3 (Medium)** | ~49% | ~71% | ~78% | | **4 (Hard)** | ~39% | ~65% | ~72% | | **5 (Very Hard)** | ~20% | ~49% | ~56% | **Trend Verification per Data Series:** * **Base Model (Purple):** Shows a steep, consistent downward trend. Accuracy starts high (~85%) for very easy tasks but drops sharply with each increase in difficulty, reaching its lowest point (~20%) at the "Very Hard" level. * **SFT Only (Pink):** Also shows a consistent downward trend, but the slope is less severe than the Base Model. It starts at a similar level to the Base Model (~84%) but maintains significantly higher accuracy at all subsequent difficulty levels. * **SFT+RL (Orange):** Exhibits the most resilient performance. While it also follows a downward trend, it consistently achieves the highest accuracy at every single difficulty level. The performance gap between SFT+RL and the other models is most pronounced at the "Very Hard" level. ### Key Observations 1. **Performance Hierarchy:** A clear and consistent hierarchy is visible across all difficulty levels: `SFT+RL > SFT Only > Base Model`. 2. **Impact of Difficulty:** All models suffer a performance drop as difficulty increases. The drop is most catastrophic for the Base Model. 3. **Widening Gap:** The absolute performance gap between the models widens as difficulty increases. For example, at "Very Easy," the difference between the best (SFT+RL) and worst (Base Model) is ~8 percentage points. At "Very Hard," this gap expands to ~36 percentage points. 4. **SFT+RL Resilience:** The SFT+RL model demonstrates the greatest robustness, retaining over 50% accuracy even on "Very Hard" tasks, a level the Base Model fails to achieve beyond "Medium" difficulty. ### Interpretation This chart provides strong evidence for the effectiveness of a training pipeline that combines Supervised Fine-Tuning (SFT) with Reinforcement Learning (RL). * **What the data suggests:** The Base Model likely represents a foundational model with general capabilities. Applying SFT ("SFT Only") provides a significant and consistent boost in accuracy, indicating that task-specific supervised training is highly beneficial. The further addition of RL ("SFT+RL") yields another substantial improvement, suggesting that RL helps the model optimize its responses in a way that is particularly advantageous for more complex, higher-difficulty problems. * **How elements relate:** The difficulty levels act as a stress test. The chart reveals that while SFT improves performance across the board, the combination of SFT and RL creates a model that is not only more accurate but also more **robust** to increasing task complexity. The widening gap implies that RL may be teaching the model more fundamental reasoning or problem-solving strategies that become critical when simple pattern matching (which might suffice for easy tasks) is no longer enough. * **Notable Anomalies:** There are no major outliers or anomalous data points. The trends are smooth and consistent, which strengthens the conclusion that the observed performance differences are a direct result of the training methodologies (Base vs. SFT vs. SFT+RL) rather than noise or error. The data presents a clear narrative of incremental improvement through advanced training techniques.

## Bar Chart: Model Accuracy Across Difficulty Levels ### Overview The chart compares the accuracy of three machine learning models (Base Model, SFT Only, SFT+RL) across five difficulty levels (Very Easy to Very Hard). Accuracy is measured in percentage, with higher values indicating better performance. ### Components/Axes - **X-axis (Difficulty Level)**: - Categories: Very Easy (1), Easy (2), Medium (3), Hard (4), Very Hard (5) - **Y-axis (Accuracy %)**: - Scale: 0% to 100% in 20% increments - **Legend**: - Top-right corner, color-coded: - Purple: Base Model - Pink: SFT Only - Orange: SFT+RL ### Detailed Analysis 1. **Very Easy (1)**: - Base Model: ~85% - SFT Only: ~84% - SFT+RL: ~93% 2. **Easy (2)**: - Base Model: ~60% - SFT Only: ~78% - SFT+RL: ~83% 3. **Medium (3)**: - Base Model: ~50% - SFT Only: ~72% - SFT+RL: ~78% 4. **Hard (4)**: - Base Model: ~40% - SFT Only: ~65% - SFT+RL: ~72% 5. **Very Hard (5)**: - Base Model: ~20% - SFT Only: ~48% - SFT+RL: ~57% ### Key Observations - **SFT+RL consistently outperforms** all other models across all difficulty levels. - **Base Model accuracy declines sharply** as difficulty increases, dropping from 85% (Very Easy) to 20% (Very Hard). - **SFT Only** maintains higher accuracy than Base Model in all categories but lags behind SFT+RL. - **Largest performance gap** occurs in "Very Hard" difficulty (SFT+RL: 57% vs. Base Model: 20%). ### Interpretation The data demonstrates that **SFT+RL (Supervised Fine-Tuning + Reinforcement Learning)** is the most robust model, maintaining high accuracy even in extremely challenging scenarios. The Base Model's performance degradation in harder tasks suggests it lacks adaptability without additional training techniques. SFT Only shows moderate improvement over Base Model, indicating that supervised fine-tuning alone provides limited benefits compared to the combined SFT+RL approach. The widening accuracy gap in higher difficulty levels highlights the importance of reinforcement learning for handling complex, real-world tasks.