## Density Plots: Token Count and Turn Distribution for Three Datasets
### Overview
The image contains two vertically stacked density plots comparing the distributions of three datasets: **SWE-Gym**, **SWE-smith**, and **Scale-SWE**. The top plot analyzes "Token Count," and the bottom plot analyzes "Turns (tool call)." Both plots use kernel density estimation to show the probability distribution of the respective metrics.
### Components/Axes
**Top Plot:**
* **X-axis:** Label: "Token Count". Scale: Linear, ranging from 0 to 120k (120,000), with major ticks at 0, 20k, 40k, 60k, 80k, 100k, 120k.
* **Y-axis:** Label: "Density". Scale: Linear, with a multiplier of **×10⁻⁵**. Major ticks at 0, 1, 2, 3 (representing 0, 1e-5, 2e-5, 3e-5).
* **Legend:** Positioned in the top-right corner. Contains three entries:
* **SWE-Gym:** Represented by a blue line and light blue filled area.
* **SWE-smith:** Represented by an orange line and light orange filled area.
* **Scale-SWE:** Represented by a green line and light green filled area.
**Bottom Plot:**
* **X-axis:** Label: "Turns (tool call)". Scale: Linear, ranging from 0 to 100, with major ticks at 0, 20, 40, 60, 80, 100.
* **Y-axis:** Label: "Density". Scale: Linear, with a multiplier of **×10⁻²**. Major ticks at 0, 1, 2 (representing 0, 0.01, 0.02).
* **Legend:** Positioned in the top-right corner. Identical to the top plot's legend.
### Detailed Analysis
**Top Plot (Token Count Distribution):**
* **SWE-Gym (Blue):** The distribution is right-skewed. It rises sharply from near 0 to a peak density of approximately **3.3e-5** at a token count of **~20k**. After the peak, it declines steadily, with a long tail extending past 100k tokens.
* **SWE-smith (Orange):** Also right-skewed. It peaks slightly earlier than SWE-Gym, at a token count of **~18k**, with a peak density of approximately **3.1e-5**. Its decline is similar to SWE-Gym but appears slightly steeper in the 20k-40k range.
* **Scale-SWE (Green):** This distribution is notably different. It is broader and shifted to the right. It begins rising later, peaks at a token count of **~40k** with a density of approximately **2.5e-5**, and has a much more gradual decline, maintaining significant density out to 80k-100k tokens.
**Bottom Plot (Turn Distribution):**
* **SWE-Gym (Blue):** The distribution is bimodal. The primary, sharp peak occurs at **~20 turns** with a density of approximately **2.7e-2**. After a steep decline, the density plateaus and then shows a smaller, secondary peak near **100 turns**.
* **SWE-smith (Orange):** The distribution is unimodal and right-skewed. It peaks at **~18 turns** with a density of approximately **2.6e-2**, closely mirroring the primary peak of SWE-Gym. It then declines steadily without a pronounced secondary peak.
* **Scale-SWE (Green):** This distribution is also bimodal but with a very different shape. It has a low, broad initial hump around **15 turns**, then rises to a major, broad peak centered around **60 turns** with a density of approximately **2.3e-2**. It then declines but shows a clear secondary peak near **100 turns**, similar to but more pronounced than SWE-Gym's.
### Key Observations
1. **Dataset Differentiation:** Scale-SWE is distinctly different from SWE-Gym and SWE-smith in both metrics. It consistently involves longer token counts and more tool-call turns.
2. **Correlation Between Metrics:** For SWE-Gym and SWE-smith, the peaks in token count (~20k) and turns (~20) align, suggesting a correlation between the length of a task (in tokens) and the number of interactive steps (turns) for these datasets.
3. **Bimodality in Turns:** Both SWE-Gym and Scale-SWE show evidence of bimodality in the turn distribution, with a secondary cluster of data points at the high end (~100 turns). This suggests a subset of tasks in these datasets require a significantly higher number of interactions.
4. **Distribution Shape:** The token count distributions for all three are unimodal and right-skewed. The turn distributions are more complex, showing unimodal (SWE-smith) and bimodal (SWE-Gym, Scale-SWE) shapes.
### Interpretation
The data suggests fundamental differences in the nature of the tasks or interactions captured by the three datasets.
* **SWE-Gym and SWE-smith** appear to represent similar types of software engineering (SWE) tasks. They are characterized by a relatively consistent, moderate length (peaking at ~20k tokens) and a similar number of interactive steps (peaking at ~20 turns). The tight coupling of these peaks implies a predictable workflow.
* **Scale-SWE** likely represents a more complex or diverse set of tasks. The right-shifted and broader token count distribution indicates tasks that are, on average, longer and more variable in length. The major peak at ~60 turns suggests these tasks require substantially more back-and-forth interaction, possibly involving more complex debugging, exploration, or multi-step problem-solving. The secondary peak at 100 turns for both Scale-SWE and SWE-Gym may indicate a specific category of "long-tail" tasks that are particularly interaction-heavy.
**In summary:** The plots reveal that Scale-SWE is a dataset of longer, more interaction-intensive SWE tasks compared to SWE-Gym and SWE-smith, which are more similar to each other. The presence of bimodal turn distributions hints at distinct task categories within the datasets, particularly one requiring a high number of tool calls.
