## Line Chart: Accuracy on ARC-C vs. Training Data
### Overview
The image is a line chart comparing the accuracy of three different methods (Online, Offline, and SFT Baseline) on the ARC-C dataset as a function of the percentage of training data used. The x-axis represents the percentage of training data, and the y-axis represents the accuracy on ARC-C.
### Components/Axes
* **X-axis:** Training Data (%), with markers at 0, 10, 20, 30, 40, 50, and 60.
* **Y-axis:** Accuracy on ARC-C, with markers at 60, 65, 70, 75, 80, 85, and 90.
* **Legend:** Located in the top-right corner, it identifies the lines as:
* Green: Online
* Blue: Offline
* Purple: SFT Baseline
### Detailed Analysis
* **Online (Green):** The accuracy increases rapidly from 60% to approximately 74% as the training data increases from 0% to 10%. It then gradually increases to approximately 76% at 30% training data, and remains relatively stable around 76% with minor fluctuations until 65% training data.
* **Offline (Blue):** The accuracy increases from 60% to approximately 71% as the training data increases from 0% to 20%. It then decreases to approximately 65% at 30% training data, and further decreases to approximately 63% at 50% training data, before increasing slightly to approximately 66% at 65% training data.
* **SFT Baseline (Purple):** The accuracy remains constant at approximately 60% across all percentages of training data.
### Key Observations
* The Online method consistently outperforms the Offline method across all training data percentages.
* The SFT Baseline method has a constant accuracy, unaffected by the amount of training data.
* The Offline method's accuracy initially increases but then decreases significantly before slightly recovering.
### Interpretation
The chart suggests that the Online method is more effective than the Offline method for this task, as it achieves higher accuracy and maintains it with increasing training data. The Offline method's performance is more volatile, suggesting it may be more sensitive to the specific training data used. The SFT Baseline method's constant accuracy indicates that it is not benefiting from increased training data, possibly due to limitations in the model's capacity or training procedure. The Online method appears to benefit most from the initial increase in training data, with diminishing returns after approximately 30% of the data is used.
