## Line Charts: Accuracy and Token Usage Across Training Progress ### Overview The image contains two side-by-side line charts comparing the performance of two methods ("Generate" and "Self-Verify") across training progress. The left chart tracks accuracy (Acc), while the right chart tracks token usage. Both charts use training progress (0 to 1) as the x-axis and distinct metrics as y-axes. --- ### Components/Axes #### Left Chart (Accuracy) - **X-axis**: Training Progress (0, 1/8, 1/4, 1/2, 1) - **Y-axis**: Accuracy (Acc) ranging from 2 to 6 - **Legend**: - Red squares: "Generate" - Blue circles: "Self-Verify" - **Legend Position**: Top-left corner #### Right Chart (Tokens) - **X-axis**: Training Progress (0, 1/8, 1/4, 1/2, 1) - **Y-axis**: Tokens ranging from 1,000 to 6,000 - **Legend**: Same as left chart (red squares for "Generate," blue circles for "Self-Verify") - **Legend Position**: Top-left corner --- ### Detailed Analysis #### Left Chart (Accuracy) - **Generate (Red Squares)**: - Starts at 2.2 (0 training progress) - Increases to 2.9 (1/8) - Peaks at 4.2 (1/2) - Drops to 2.5 (1) - **Self-Verify (Blue Circles)**: - Starts at 2.2 (0 training progress) - Increases to 3.5 (1/4) - Peaks at 5.6 (1/2) - Drops to 4.0 (1) #### Right Chart (Tokens) - **Generate (Red Squares)**: - Starts at 1,935 (0 training progress) - Increases to 2,580 (1/8) - Rises to 2,910 (1/4) - Peaks at 3,782 (1/2) - Ends at 5,422 (1) - **Self-Verify (Blue Circles)**: - Starts at 1,935 (0 training progress) - Drops to 1,635 (1/8) - Rises to 1,754 (1/4) - Increases to 3,228 (1) --- ### Key Observations 1. **Accuracy Trends**: - "Generate" accuracy peaks at 1/2 training progress (4.2) but declines sharply by 1 (2.5). - "Self-Verify" accuracy peaks earlier (1/2 training progress, 5.6) and stabilizes at 4.0 by 1. - "Self-Verify" consistently outperforms "Generate" after 1/4 training progress. 2. **Token Usage Trends**: - "Generate" token usage increases monotonically, doubling from 1,935 to 5,422. - "Self-Verify" token usage dips at 1/8 (1,635) but recovers to 3,228 by 1, showing a net increase of 65%. - "Generate" uses significantly more tokens than "Self-Verify" at all stages. 3. **Divergence at 1/2 Training Progress**: - "Generate" reaches its peak accuracy (4.2) and token usage (3,782) at 1/2. - "Self-Verify" achieves higher accuracy (5.6) with fewer tokens (1,754) at this stage. --- ### Interpretation 1. **Performance Trade-offs**: - "Self-Verify" achieves higher accuracy with lower token costs, suggesting greater efficiency. Its accuracy peaks earlier and remains stable, while "Generate" overfits or degrades later in training. - The token usage divergence implies "Generate" may be computationally expensive, while "Self-Verify" balances accuracy and resource use. 2. **Training Dynamics**: - The sharp drop in "Generate" accuracy after 1/2 training progress could indicate overfitting or instability in later stages. - "Self-Verify"’s mid-training peak (1/2) suggests a more robust learning curve, possibly due to iterative verification reducing noise. 3. **Anomalies**: - The "Self-Verify" token dip at 1/8 (1,635) is unusual but recovers quickly, hinting at a transient inefficiency or optimization phase. --- ### Conclusion The data demonstrates that "Self-Verify" outperforms "Generate" in both accuracy and token efficiency, particularly in later training stages. This suggests that self-verification mechanisms may enhance model reliability while optimizing computational resources. Further investigation into the causes of the "Generate" accuracy drop and "Self-Verify" token dip could refine training strategies.