## Line Graphs: AI Model Accuracy Across Datasets ### Overview The image contains three line graphs comparing the accuracy of four AI models (ReasonFlux-PRM-7B, Gwen2.5-Math-PRM-7B, Skywork-PRM-7B, and Majority) across three datasets (AIME24, MATH500, GPQA-Diamond). Accuracy (%) is plotted against the number of solutions (N = 2¹ to 2⁴). Each graph shows distinct performance trends, with ReasonFlux-PRM-7B consistently outperforming other models. --- ### Components/Axes - **X-axis**: "Number of Solutions (N)" with values 2¹, 2², 2³, 2⁴. - **Y-axis**: "Accuracy (%)" ranging from ~35% to ~92% depending on the dataset. - **Legend**: - Blue: ReasonFlux-PRM-7B - Orange: Gwen2.5-Math-PRM-7B - Green: Skywork-PRM-7B - Red: Majority - **Datasets**: - AIME24 (left graph) - MATH500 (center graph) - GPQA-Diamond (right graph) --- ### Detailed Analysis #### AIME24 Dataset - **ReasonFlux-PRM-7B (Blue)**: Starts at ~40% (2¹), increases steadily to ~49% (2⁴). Steepest slope. - **Gwen2.5-Math-PRM-7B (Orange)**: Flat line at ~44% across all N values. - **Skywork-PRM-7B (Green)**: Starts at ~40%, rises to ~47% (2⁴). Moderate slope. - **Majority (Red)**: Starts at ~35%, increases to ~43% (2⁴). Gradual upward trend. #### MATH500 Dataset - **ReasonFlux-PRM-7B (Blue)**: Starts at ~85%, peaks at ~92% (2⁴). Strong upward trajectory. - **Gwen2.5-Math-PRM-7B (Orange)**: Begins at ~85%, rises to ~89% (2⁴). Steady growth. - **Skywork-PRM-7B (Green)**: Starts at ~85%, increases to ~88% (2⁴). Slight upward curve. - **Majority (Red)**: Flat line at ~86% across all N values. #### GPQA-Diamond Dataset - **ReasonFlux-PRM-7B (Blue)**: Starts at ~48%, climbs to ~55% (2⁴). Consistent growth. - **Gwen2.5-Math-PRM-7B (Orange)**: Begins at ~48%, rises to ~53% (2⁴). Moderate slope. - **Skywork-PRM-7B (Green)**: Starts at ~48%, increases to ~51% (2⁴). Gradual rise. - **Majority (Red)**: Starts at ~48%, peaks at ~49% (2⁴). Minimal improvement. --- ### Key Observations 1. **ReasonFlux-PRM-7B Dominance**: Outperforms all models in all datasets, with the steepest accuracy gains in AIME24 and MATH500. 2. **Majority Model Limitations**: Shows the lowest accuracy (35–49%) and minimal improvement across datasets. 3. **Dataset-Specific Performance**: - **AIME24**: ReasonFlux gains ~9% accuracy (2¹→2⁴), while Gwen2.5 remains flat. - **MATH500**: ReasonFlux achieves ~7% gain, Gwen2.5 ~4%. - **GPQA-Diamond**: ReasonFlux gains ~7%, Gwen2.5 ~5%. 4. **Majority Model Stagnation**: No significant improvement in any dataset, suggesting it may represent a baseline or less adaptive approach. --- ### Interpretation The data demonstrates that **ReasonFlux-PRM-7B** excels in reasoning tasks across diverse datasets, likely due to advanced problem-solving capabilities. Its performance scales effectively with increased solution complexity (N). In contrast, the **Majority model** underperforms, possibly reflecting a simpler or less optimized architecture. The **Gwen2.5-Math-PRM-7B** model shows dataset-specific strengths (e.g., flat performance in AIME24 but steady gains in MATH500), indicating potential specialization in mathematical reasoning. The **Skywork-PRM-7B** model bridges the gap between ReasonFlux and Majority, suggesting moderate adaptability. These trends highlight the importance of model architecture design for task-specific accuracy.