## Line Chart: Model Success Rate vs. Training Examples
### Overview
This image is a line chart comparing the performance of three different models or methods (Wan2.2†, Wan2.1†, and SVD†) as a function of the number of examples seen during training. The chart plots "Success Rate (%)" against "Seen Examples During Training" on a logarithmic scale. The data suggests an analysis of model learning efficiency or scaling behavior.
### Components/Axes
* **X-Axis (Horizontal):** Labeled **"Seen Examples During Training"**. It uses a logarithmic scale with major tick marks at **400**, **4K** (4,000), **40K** (40,000), and **80K** (80,000).
* **Y-Axis (Vertical):** Labeled **"Success Rate (%)"**. It uses a linear scale ranging from **52** to **64**, with major tick marks at every integer value.
* **Legend:** Located in the **bottom-right corner** of the chart area. It contains three entries:
* **Wan2.2†**: Represented by a **yellow star (★)** symbol.
* **Wan2.1†**: Represented by a **green line with circular markers (●)**.
* **SVD†**: Represented by a **blue line with square markers (■)**.
* **Data Series & Points:**
* **Wan2.1† (Green Line):** A solid green line connecting four circular data points.
* **SVD† (Blue Line):** A solid blue line connecting four square data points.
* **Wan2.2† (Yellow Star):** A single, isolated data point marked with a star.
### Detailed Analysis
**Data Series: Wan2.1† (Green Line with Circles)**
* **Trend:** The line shows a consistent, gradual upward slope from left to right, indicating a steady increase in success rate with more training examples.
* **Data Points:**
* At **400** examples: **60.25%**
* At **4K** examples: **61.52%**
* At **40K** examples: **62.61%**
* At **80K** examples: **63.34%**
**Data Series: SVD† (Blue Line with Squares)**
* **Trend:** The line shows a very shallow increase between 400 and 4K examples, followed by a steep upward slope between 4K and 40K examples, and then plateaus (flattens) between 40K and 80K examples.
* **Data Points:**
* At **400** examples: **56.26%**
* At **4K** examples: **56.44%**
* At **40K** examples: **60.98%**
* At **80K** examples: **60.98%**
**Data Point: Wan2.2† (Yellow Star)**
* **Placement:** This is a single data point located at the **40K** examples mark on the x-axis.
* **Value:** **62.61%**. This value is identical to the Wan2.1† data point at the same x-axis position.
### Key Observations
1. **Performance Hierarchy:** At all measured points, the Wan2.1† model (green) achieves a higher success rate than the SVD† model (blue).
2. **Convergence at 40K:** At 40,000 training examples, the performance of Wan2.1† and Wan2.2† is identical (62.61%).
3. **Diminishing Returns for SVD†:** The SVD† model shows no improvement in success rate between 40K and 80K examples, suggesting a performance plateau.
4. **Continuous Improvement for Wan2.1†:** The Wan2.1† model continues to show measurable improvement (from 62.61% to 63.34%) when scaling from 40K to 80K examples.
5. **Initial Gap:** The initial performance gap at 400 examples between Wan2.1† (60.25%) and SVD† (56.26%) is approximately **4 percentage points**.
### Interpretation
This chart demonstrates the scaling laws or data efficiency of different models. The **Wan2.1†** model exhibits superior and more consistent learning, maintaining a positive growth trajectory across the entire observed range. The **SVD†** model benefits significantly from increased data between 4K and 40K examples but hits a ceiling, indicating it may be a less scalable approach or has reached its capacity with the given architecture.
The single data point for **Wan2.2†** is intriguing. Its performance matching Wan2.1† at 40K examples could imply it is a variant or an optimized version that achieves comparable results at that specific data scale. The absence of data points for Wan2.2† at other scales leaves its overall scaling behavior unknown.
**Underlying Message:** For the task measured by "Success Rate," investing in more training data is beneficial for all models, but the returns are model-dependent. Wan2.1† appears to be the most robust and scalable choice among the three, as it continues to improve where SVD† stagnates. The chart provides empirical evidence to guide decisions on model selection and data collection budgets.
