## Line Chart: Accuracy vs. Step for RLVR and RLME ### Overview This image displays a line chart comparing the performance (Accuracy) of two distinct methods, labeled "RLVR" and "RLME," over a training duration of 120 steps. The chart illustrates the learning progression of both models, showing a rapid increase in accuracy followed by a plateau. ### Components/Axes * **Y-Axis:** Labeled "Accuracy." The scale ranges from 0.2 to 1.0, with major grid lines at 0.2 intervals. * **X-Axis:** Labeled "Step." The scale ranges from 0 to 120, with major grid lines at 20-step intervals. * **Legend:** Located in the bottom-right quadrant of the chart area. * **RLVR:** Represented by a dotted grey line. * **RLME:** Represented by a solid blue line, accompanied by a light blue shaded region representing the confidence interval or variance. ### Detailed Analysis **Trend Verification:** * **RLVR (Dotted Grey Line):** Shows a steep, consistent upward trend from Step 0 to Step 40. After Step 40, the line plateaus, maintaining a relatively stable accuracy between 0.9 and 0.95 for the remainder of the steps. * **RLME (Solid Blue Line):** Shows a steep upward trend from Step 0 to Step 40, though slightly less aggressive than RLVR. After Step 40, it plateaus around 0.9. The light blue shaded region indicates variance, which appears relatively consistent throughout the training process. **Data Point Estimates:** * **Step 0:** RLVR starts at approximately 0.25; RLME starts at approximately 0.35. * **Step 20:** RLVR reaches approximately 0.75; RLME reaches approximately 0.70. * **Step 40:** RLVR reaches approximately 0.92; RLME reaches approximately 0.89. * **Step 60:** RLVR reaches approximately 0.94; RLME reaches approximately 0.91. * **Step 120:** RLVR maintains approximately 0.93; RLME maintains approximately 0.89. ### Key Observations * **Performance Crossover:** RLME begins with higher accuracy at Step 0, but RLVR overtakes RLME very early (around Step 10) and maintains a higher accuracy for the duration of the chart. * **Convergence:** Both models exhibit rapid learning, with the majority of accuracy gains occurring within the first 40 steps. * **Stability:** RLVR appears to achieve a slightly higher final accuracy (approx. 0.93–0.94) compared to RLME (approx. 0.89). * **Variance:** The RLME method includes a shaded region indicating uncertainty or variance in its performance, whereas the RLVR method is plotted as a single line without a visible variance band. ### Interpretation The data demonstrates that while RLME starts with a slight initial advantage, RLVR is the superior method in terms of final accuracy and learning efficiency after the initial warm-up phase. The rapid plateauing after 40 steps suggests that both models reach their peak performance relatively quickly in this training environment. The lack of a shaded region for RLVR might imply either lower variance in its results or simply a difference in how the data was visualized compared to RLME. Overall, RLVR is the more performant model in this specific context.