## Line Chart: Qwen2.5-7B ### Overview The image is a line chart comparing the performance of two models, "Base" and "Instruct," on a "Lichess Puzzle Acc" metric over a range of "Training Steps." The chart displays the accuracy of each model as it increases with training. ### Components/Axes * **Title:** Qwen2.5-7B * **X-axis:** Training Step, with markers at 0, 30, 60, 90, 120, and 150. * **Y-axis:** Lichess Puzzle Acc, with markers at 0.00, 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30. * **Legend:** Located in the bottom-right corner. * Blue line: Base * Gray line: Instruct ### Detailed Analysis * **Base (Blue):** The blue line represents the "Base" model. It starts at approximately 0.01 accuracy at training step 0. The line slopes upward, reaching approximately 0.18 at step 30, 0.23 at step 60, 0.28 at step 90, and plateaus around 0.29 at step 120 and 150. * **Instruct (Gray):** The gray line represents the "Instruct" model. It starts at approximately 0.02 accuracy at training step 0. The line slopes upward, reaching approximately 0.20 at step 30, 0.29 at step 60, and plateaus around 0.30 at step 90, 120, and 150. ### Key Observations * Both models show an increase in "Lichess Puzzle Acc" as the "Training Step" increases. * The "Instruct" model consistently outperforms the "Base" model throughout the training steps. * Both models exhibit a period of rapid improvement in accuracy, followed by a plateau as training progresses. ### Interpretation The chart demonstrates the impact of training on the performance of two models, "Base" and "Instruct," in solving Lichess puzzles. The "Instruct" model, presumably trained with specific instructions or a different training methodology, consistently achieves higher accuracy than the "Base" model. The plateauing of both lines suggests that further training beyond a certain point yields diminishing returns in terms of accuracy improvement. The "Instruct" model's superior performance indicates that the training approach used for this model is more effective for this particular task.

\n ## Line Chart: Qwen2.5-7B Lichess Puzzle Accuracy vs. Training Step ### Overview This line chart displays the Lichess Puzzle Accuracy (y-axis) of two models, "Base" and "Instruct", over a range of Training Steps (x-axis). The chart aims to compare the performance of the two models as they are trained. The title of the chart is "Qwen2.5-7B". ### Components/Axes * **Title:** Qwen2.5-7B (positioned at the top-center) * **X-axis:** Training Step (ranging from 0 to 150, with tick marks every 30 steps) * **Y-axis:** Lichess Puzzle Acc (ranging from 0.00 to 0.30, with tick marks every 0.05) * **Legend:** Located in the bottom-right corner, containing two entries: * "Base" - represented by a solid blue line. * "Instruct" - represented by a solid gray line. * **Gridlines:** Horizontal and vertical gridlines are present to aid in reading values. ### Detailed Analysis **Base Model (Blue Line):** The blue line representing the "Base" model starts at approximately 0.00 at Training Step 0. It exhibits a steep upward slope from 0 to 30 Training Steps, reaching approximately 0.15. The slope then decreases, becoming more gradual from 30 to 150 Training Steps. At 60 Training Steps, the accuracy is approximately 0.24. At 90 Training Steps, the accuracy is approximately 0.27. At 120 Training Steps, the accuracy is approximately 0.28. At 150 Training Steps, the accuracy plateaus at approximately 0.29. **Instruct Model (Gray Line):** The gray line representing the "Instruct" model also starts at approximately 0.00 at Training Step 0. It shows a rapid increase in accuracy from 0 to 30 Training Steps, reaching approximately 0.25. The slope then decreases, becoming more gradual from 30 to 150 Training Steps. At 60 Training Steps, the accuracy is approximately 0.29. At 90 Training Steps, the accuracy is approximately 0.30. At 120 Training Steps, the accuracy is approximately 0.31. At 150 Training Steps, the accuracy plateaus at approximately 0.31. ### Key Observations * The "Instruct" model consistently outperforms the "Base" model across all Training Steps. * Both models exhibit diminishing returns in accuracy as the number of Training Steps increases. The initial gains are significant, but the rate of improvement slows down considerably after 30 Training Steps. * The "Instruct" model reaches a plateau in accuracy earlier than the "Base" model. * The difference in accuracy between the two models is most pronounced in the early stages of training (0-60 steps). ### Interpretation The data suggests that the "Instruct" model benefits significantly from the training process, achieving higher Lichess Puzzle Accuracy compared to the "Base" model. The initial rapid increase in accuracy for both models indicates that the models are quickly learning from the training data. The subsequent plateau suggests that the models are approaching their maximum performance level, or that the training data is no longer providing significant new information. The consistent outperformance of the "Instruct" model implies that the instruction-based training approach is more effective for this specific task (Lichess puzzle solving) than the base training approach. The diminishing returns observed after 60 training steps suggest that further training may not yield substantial improvements in accuracy. This could be due to the limitations of the model architecture, the quality of the training data, or the optimization algorithm used.

## Line Graph: Qwen2.5-7B Model Performance Over Training Steps ### Overview The graph compares the Lichess Puzzle Accuracy (Acc) of two models ("Base" and "Instruct") during training. Both models show increasing performance over time, with "Instruct" consistently outperforming "Base" across all training steps. ### Components/Axes - **Title**: "Qwen2.5-7B" - **X-axis**: "Training Step" (0 to 150, increments of 30) - **Y-axis**: "Lichess Puzzle Accuracy (Acc)" (0.00 to 0.30, increments of 0.05) - **Legend**: Located in the bottom-right corner, with: - **Blue line**: "Base" model - **Gray line**: "Instruct" model ### Detailed Analysis 1. **Base Model (Blue Line)**: - Starts at **0.00** at 0 training steps. - Gradual increase to **~0.29** at 150 steps. - Key points: - 30 steps: **~0.12** - 60 steps: **~0.23** - 90 steps: **~0.27** - 120 steps: **~0.28** - 150 steps: **~0.29** 2. **Instruct Model (Gray Line)**: - Starts at **~0.02** at 0 training steps. - Steeper ascent to **~0.32** at 150 steps. - Key points: - 30 steps: **~0.20** - 60 steps: **~0.28** - 90 steps: **~0.30** - 120 steps: **~0.31** - 150 steps: **~0.32** ### Key Observations - **Performance Gap**: "Instruct" maintains a **~0.03–0.04 accuracy advantage** over "Base" throughout training. - **Convergence**: The gap narrows slightly in later stages (e.g., from **0.09** at 30 steps to **0.03** at 150 steps). - **Plateauing**: Both models show diminishing returns after ~90 steps, with "Instruct" reaching a higher plateau. ### Interpretation The data suggests that the "Instruct" model's architecture or training methodology inherently enables superior performance in Lichess puzzles compared to the "Base" model. The narrowing gap at later stages implies that while "Base" improves with training, "Instruct" benefits from a more efficient learning process or better initialization. This could reflect differences in: - **Training data curation** (e.g., "Instruct" may use more targeted examples). - **Model design** (e.g., "Instruct" might incorporate reinforcement learning or human feedback). - **Regularization techniques** (e.g., "Instruct" could avoid overfitting more effectively). The consistent outperformance of "Instruct" highlights the importance of architectural choices in specialized tasks like chess puzzle solving. Further analysis could explore whether this trend holds across other domains or if it is specific to chess-related reasoning.