## Line Chart: Mean Success Rate Across Checkpoints ### Overview The chart compares the mean success rates of two policies ("Video Policy" and "Diffusion Policy") across training checkpoints (0 to 60k steps). Success rate is measured in percentage, with confidence intervals shaded around each line. ### Components/Axes - **X-axis**: Checkpoint (training steps) labeled at 0, 10k, 20k, 40k, and 60k. - **Y-axis**: Success Rate (%) ranging from 0 to 35% in 5% increments. - **Legend**: Located in the top-left corner, with: - **Blue line with circles**: Video Policy - **Orange line with squares**: Diffusion Policy ### Detailed Analysis #### Video Policy (Blue) - **Trend**: Steady upward trajectory with a sharp increase between 10k and 40k checkpoints. - **Data Points**: - 0k: ~19% (confidence interval: 18–20%) - 10k: ~20% (confidence interval: 19–21%) - 20k: ~26% (confidence interval: 24–28%) - 40k: ~29% (confidence interval: 27–31%) #### Diffusion Policy (Orange) - **Trend**: Gradual rise with fluctuations, followed by a sharp increase after 40k. - **Data Points**: - 10k: ~4% (confidence interval: 3–5%) - 20k: ~10% (confidence interval: 9–11%) - 40k: ~8% (confidence interval: 7–9%) - 60k: ~15% (confidence interval: 14–16%) ### Key Observations 1. **Video Policy Dominance**: Consistently outperforms Diffusion Policy across all checkpoints, especially after 20k steps. 2. **Diffusion Policy Volatility**: Success rate fluctuates significantly (e.g., drops from 10% at 20k to 8% at 40k) before recovering. 3. **Confidence Intervals**: Video Policy’s wider shaded area suggests higher variability in early checkpoints, narrowing as training progresses. ### Interpretation - **Performance Dynamics**: Video Policy demonstrates robust learning efficiency, achieving ~29% success by 40k steps. Diffusion Policy lags initially but shows potential for improvement, reaching ~15% by 60k steps. - **Stability vs. Exploration**: The narrowing confidence intervals for Video Policy imply stabilizing performance, while Diffusion Policy’s wider intervals at later stages suggest ongoing exploration or instability. - **Practical Implications**: Video Policy may be preferable for applications requiring early convergence, whereas Diffusion Policy might benefit from extended training to mitigate volatility. *Note: All values are approximate, derived from visual inspection of the chart.*