## Chart: Test Result on OlympiadBench ### Overview The image is a line graph titled "Test Result on OlympiadBench". It shows the average benchmark accuracy (%) on the y-axis versus the training steps of reinforcement learning on the x-axis. There are two data series plotted: "Turn 1 Accuracy" and "Final Accuracy". ### Components/Axes * **Title:** Test Result on OlympiadBench * **X-axis:** Training Steps of Reinforcement Learning * Scale: 0 to 200, with tick marks at 0, 50, 100, 150, and 200. * **Y-axis:** Average Benchmark Accuracy (%) * Scale: 35 to 44, with tick marks at each integer value. * **Legend:** Located in the top-left corner. * "Turn 1 Accuracy" is represented by a green line. * "Final Accuracy" is represented by a dark blue line. ### Detailed Analysis * **Turn 1 Accuracy (Green Line):** * Trend: Generally increasing with some fluctuations. * Data Points: * At 0 steps: ~35.4% * At 25 steps: ~35.3% * At 50 steps: ~36.8% * At 75 steps: ~38.2% * At 100 steps: ~39.2% * At 125 steps: ~38.1% * At 150 steps: ~39.0% * At 175 steps: ~40.3% * At 200 steps: ~40.1% * At 225 steps: ~41.0% * **Final Accuracy (Dark Blue Line):** * Trend: Fluctuating, but generally increasing. * Data Points: * At 0 steps: ~39.4% * At 25 steps: ~38.6% * At 50 steps: ~39.8% * At 75 steps: ~40.6% * At 100 steps: ~42.5% * At 125 steps: ~40.1% * At 150 steps: ~40.4% * At 175 steps: ~42.4% * At 200 steps: ~42.1% * At 225 steps: ~43.3% ### Key Observations * The "Final Accuracy" starts higher than the "Turn 1 Accuracy". * Both accuracies generally increase with training steps. * The "Final Accuracy" fluctuates more than the "Turn 1 Accuracy". * The "Final Accuracy" appears to plateau or slightly decrease towards the end of the training steps. ### Interpretation The graph illustrates the performance of a reinforcement learning model on the OlympiadBench dataset. The "Turn 1 Accuracy" represents the accuracy of the model after the first turn, while the "Final Accuracy" represents the accuracy after all turns. The increasing trend in both accuracies suggests that the model is learning and improving its performance as it is trained. The fluctuations in the "Final Accuracy" could be due to the stochastic nature of reinforcement learning or the complexity of the task. The fact that the "Final Accuracy" starts higher than the "Turn 1 Accuracy" indicates that the model is already performing reasonably well at the beginning of training. The plateauing of the "Final Accuracy" towards the end of training suggests that the model may be approaching its maximum performance on this dataset.

\n ## Line Chart: Test Result on OlympiadBench ### Overview This image presents a line chart illustrating the performance of a model on the OlympiadBench benchmark, measured by "Turn 1 Accuracy" and "Final Accuracy" as a function of "Training Steps of Reinforcement Learning". The chart displays the evolution of these accuracy metrics over approximately 200 training steps. ### Components/Axes * **Title:** "Test Result on OlympiadBench" (Top-center) * **X-axis:** "Training Steps of Reinforcement Learning" (Bottom-center), ranging from 0 to 200, with markers at increments of 50. * **Y-axis:** "Average Benchmark Accuracy (%)" (Left-center), ranging from 35% to 44%, with markers at increments of 1. * **Legend:** Located at the top-left corner. * "Turn 1 Accuracy" - Green line with triangle markers. * "Final Accuracy" - Blue line with circle markers. ### Detailed Analysis **Turn 1 Accuracy (Green Line):** The green line representing "Turn 1 Accuracy" exhibits an overall upward trend, but with significant fluctuations. * At 0 training steps, the accuracy is approximately 35.2%. * It increases to around 36.5% at 50 training steps. * It reaches a local maximum of approximately 39.5% at 125 training steps. * It dips to around 38.5% at 150 training steps. * Finally, it rises to approximately 40.5% at 200 training steps. **Final Accuracy (Blue Line):** The blue line representing "Final Accuracy" also shows an upward trend, but with more pronounced peaks and valleys. * At 0 training steps, the accuracy is approximately 40.2%. * It decreases to around 38.8% at 50 training steps. * It peaks at approximately 42.5% at 100 training steps. * It drops to around 40.2% at 150 training steps. * It rises to approximately 42.4% at 175 training steps. * It reaches a maximum of approximately 43.5% at 200 training steps. ### Key Observations * "Final Accuracy" consistently outperforms "Turn 1 Accuracy" throughout the training process. * Both accuracy metrics demonstrate a non-linear improvement, with periods of rapid growth followed by plateaus or declines. * The "Final Accuracy" line shows a more volatile pattern than the "Turn 1 Accuracy" line, suggesting that the final result is more sensitive to the training process. * The largest increase in "Final Accuracy" occurs between 50 and 100 training steps. * The final accuracy at 200 steps is approximately 3.3% higher than the initial accuracy at 0 steps. ### Interpretation The chart suggests that the reinforcement learning model improves its performance on the OlympiadBench benchmark as training progresses. The difference between "Turn 1 Accuracy" and "Final Accuracy" indicates that the model refines its solutions over multiple turns. The fluctuations in accuracy suggest that the training process is not always smooth and may be affected by factors such as the exploration-exploitation trade-off or the stochasticity of the environment. The overall upward trend demonstrates that the model is learning and adapting to the task. The fact that "Final Accuracy" is consistently higher than "Turn 1 Accuracy" suggests that the model benefits from iterative refinement of its initial solutions. The data suggests that continued training beyond 200 steps might yield further improvements, but the rate of improvement may diminish.

## Line Chart: Final Result on OlympiadBench ### Overview The image displays a line chart titled "Final Result on OlympiadBench." It plots the performance of two accuracy metrics over the course of reinforcement learning training steps. The chart shows a general upward trend for both metrics, indicating improvement with increased training. ### Components/Axes * **Chart Title:** "Final Result on OlympiadBench" (centered at the top). * **X-Axis:** Labeled "Training Steps of Reinforcement Learning." The axis has major tick marks at intervals of 50, labeled: 0, 50, 100, 150, 200. * **Y-Axis:** Labeled "Average Benchmark Accuracy (%)". The axis has major tick marks at intervals of 1, labeled from 35 to 44. * **Legend:** Located in the top-left corner of the chart area. It contains two entries: * A green line labeled "Sum. M Accuracy" * A blue line labeled "Final Accuracy" * **Data Series:** Two lines plotted on the chart: 1. A **green line** representing "Sum. M Accuracy." 2. A **blue line** representing "Final Accuracy." ### Detailed Analysis **Data Series 1: Sum. M Accuracy (Green Line)** * **Trend:** The green line shows a generally positive, upward trend with moderate fluctuations. It starts at the lowest point on the chart and ends significantly higher. * **Approximate Data Points:** * Step 0: ~35.5% * Step 25: ~35.8% * Step 50: ~36.5% * Step 75: ~37.5% * Step 100: ~39.0% (local peak) * Step 125: ~38.0% (dip) * Step 150: ~39.5% * Step 175: ~39.8% * Step 200: ~40.5% **Data Series 2: Final Accuracy (Blue Line)** * **Trend:** The blue line also shows a positive, upward trend but is more volatile than the green line. It consistently remains above the green line throughout the training steps. * **Approximate Data Points:** * Step 0: ~39.5% * Step 25: ~39.0% (dip) * Step 50: ~40.5% * Step 75: ~41.0% * Step 100: ~42.0% (significant peak) * Step 125: ~40.0% (sharp dip) * Step 150: ~41.5% * Step 175: ~42.0% * Step 200: ~43.0% (highest point) ### Key Observations 1. **Consistent Performance Gap:** The "Final Accuracy" (blue) is consistently higher than the "Sum. M Accuracy" (green) at every measured training step. The gap between them is approximately 3-4 percentage points. 2. **Correlated Movements:** Both lines often move in tandem. For example, both show a local peak at step 100 and a subsequent dip at step 125, suggesting a common factor affecting both metrics at those training stages. 3. **Peak Performance:** Both metrics achieve their highest values at the final recorded step (200), with "Final Accuracy" reaching ~43% and "Sum. M Accuracy" reaching ~40.5%. 4. **Volatility:** The "Final Accuracy" line exhibits sharper peaks and valleys (e.g., the pronounced peak at step 100 and dip at step 125) compared to the somewhat smoother progression of the "Sum. M Accuracy" line. ### Interpretation The chart demonstrates the effectiveness of reinforcement learning training on the OlympiadBench benchmark. The upward trajectory of both lines indicates that the model's performance improves as it undergoes more training steps. The persistent gap between "Final Accuracy" and "Sum. M Accuracy" suggests these are measuring different aspects of performance. "Final Accuracy" likely represents the model's ultimate answer accuracy, while "Sum. M Accuracy" might be a component score or a metric from an intermediate step (e.g., summarization or multiple-choice accuracy). The fact that the final answer accuracy is higher implies the model may be effectively synthesizing or correcting intermediate outputs to arrive at better final answers. The correlated dip after step 100 is a notable anomaly. This could indicate a period of instability in training, such as the model encountering a particularly challenging subset of data, a change in the learning rate, or a temporary overfitting phenomenon before recovering and continuing to improve. The overall trend, however, is positive, showing that extended training (up to 200 steps) yields better results on this benchmark.

## Line Chart: Test Result on OlympiadBench ### Overview The image is a line chart titled "Test Result on OlympiadBench," comparing two metrics: **Turn 1 Accuracy** (green line) and **Final Accuracy** (blue line) across **Training Steps of Reinforcement Learning** (x-axis). The y-axis represents **Average Benchmark Accuracy (%)**, ranging from 35% to 44%. The chart shows performance trends over 200 training steps, with both metrics generally increasing but exhibiting fluctuations. --- ### Components/Axes - **X-axis**: "Training Steps of Reinforcement Learning" (0 to 200, in increments of 50). - **Y-axis**: "Average Benchmark Accuracy (%)" (35% to 44%, in 1% increments). - **Legend**: - **Turn 1 Accuracy**: Green line (bottom-left placement). - **Final Accuracy**: Blue line (top-left placement). --- ### Detailed Analysis #### Turn 1 Accuracy (Green Line) - **Initial Value**: ~35.5% at 0 steps. - **Trend**: Gradual increase with minor fluctuations. - Peaks at ~39% around 100 steps. - Stabilizes between ~38% and ~40% after 150 steps. - **Final Value**: ~40.5% at 200 steps. #### Final Accuracy (Blue Line) - **Initial Value**: ~38.5% at 0 steps. - **Trend**: Steeper growth with volatility. - Sharp peak at ~42.5% around 100 steps. - Dips to ~40% at 125 steps, then rises to ~43% by 200 steps. - **Final Value**: ~43.5% at 200 steps. --- ### Key Observations 1. **Final Accuracy consistently exceeds Turn 1 Accuracy** across all steps. 2. **Both metrics show improvement** with more training steps, but **Final Accuracy has greater variability** (e.g., sharp peaks and troughs). 3. **Highest performance** for both metrics occurs around **100 steps**, followed by stabilization. 4. **Turn 1 Accuracy** plateaus earlier (~150 steps) compared to **Final Accuracy**, which continues improving until 200 steps. --- ### Interpretation The data suggests that **reinforcement learning improves performance over time**, with **Final Accuracy** reflecting a more robust or optimized model state. The **Turn 1 Accuracy** likely represents initial, less refined results, while **Final Accuracy** captures the model's stabilized performance after iterative training. The **volatility in Final Accuracy** (e.g., the dip at 125 steps) may indicate challenges in convergence or sensitivity to hyperparameters. The **higher final value** (~43.5% vs. ~40.5%) underscores the value of extended training, though diminishing returns are evident after 150 steps. This chart highlights the trade-off between **early-stage performance** (Turn 1) and **long-term optimization** (Final Accuracy), critical for evaluating reinforcement learning strategies in benchmark tasks.