## Line Chart: HumanEval vs. Tokens Trained ### Overview The image is a line chart comparing "HumanEval" scores against "Tokens Trained (Billion)" for different configurations denoted as "Rec" (likely referring to some kind of "recurrent" or "recognition" parameter). There are six data series, each representing a different "Rec" value: 1, 4, 8, 16, 32, and 64. The chart shows how the HumanEval score changes as the number of tokens trained increases for each configuration. ### Components/Axes * **X-axis:** "Tokens Trained (Billion)". The scale ranges from 100 to 800, with tick marks at intervals of 100. * **Y-axis:** "HumanEval". The scale ranges from 0 to 25, with tick marks at intervals of 5. * **Legend:** Located at the top of the chart, it identifies each line by its "Rec" value and corresponding color/linestyle: * **Blue:** 1 Rec (solid line) * **Orange:** 4 Rec (dashed line) * **Green:** 8 Rec (dash-dotted line) * **Red:** 16 Rec (dotted line) * **Purple:** 32 Rec (solid line) * **Brown:** 64 Rec (dashed line) ### Detailed Analysis * **1 Rec (Blue, Solid):** The line remains almost flat at a value of approximately 0 for all token values. * (100, ~0) * (800, ~0) * **4 Rec (Orange, Dashed):** The line starts near 0, increases slightly, and then fluctuates between 1 and 4. * (100, ~0) * (200, ~0.5) * (300, ~1) * (400, ~3) * (500, ~2) * (600, ~1) * (700, ~4) * (800, ~1) * **8 Rec (Green, Dash-Dotted):** The line increases from approximately 2 to 15, then decreases to approximately 12. * (100, ~2) * (200, ~7) * (300, ~8) * (400, ~10) * (500, ~11) * (600, ~15) * (700, ~15) * (800, ~12) * **16 Rec (Red, Dotted):** The line increases from approximately 6 to 19, then plateaus around 19. * (100, ~6) * (200, ~9) * (300, ~12) * (400, ~15) * (500, ~18) * (600, ~19) * (700, ~19) * (800, ~19) * **32 Rec (Purple, Solid):** The line increases from approximately 6 to 23. * (100, ~6) * (200, ~9) * (300, ~13) * (400, ~15) * (500, ~15) * (600, ~19) * (700, ~19) * (800, ~23) * **64 Rec (Brown, Dashed):** The line increases from approximately 6 to 23. * (100, ~6) * (200, ~8) * (300, ~12) * (400, ~15) * (500, ~15) * (600, ~19) * (700, ~19) * (800, ~23) ### Key Observations * The "1 Rec" configuration performs significantly worse than all other configurations, with HumanEval scores consistently near 0. * The "4 Rec" configuration also performs poorly, with HumanEval scores generally below 5. * The "8 Rec" configuration shows an initial increase in HumanEval score, but then decreases after 600 Billion tokens trained. * The "16 Rec", "32 Rec", and "64 Rec" configurations show similar performance, with HumanEval scores increasing significantly as the number of tokens trained increases. The "16 Rec" plateaus around 19, while "32 Rec" and "64 Rec" continue to increase to approximately 23. ### Interpretation The chart suggests that the "Rec" parameter has a significant impact on the HumanEval score. Lower values of "Rec" (1 and 4) result in poor performance, while higher values (16, 32, and 64) lead to significantly better results. The "8 Rec" configuration shows a non-monotonic relationship, suggesting that there may be an optimal value for this parameter. The plateauing of the "16 Rec" configuration suggests that there may be diminishing returns to training beyond a certain point for this configuration. The continued increase of "32 Rec" and "64 Rec" suggests that these configurations may benefit from further training.

\n ## Line Chart: HumanEval Performance vs. Tokens Trained ### Overview This line chart depicts the relationship between the number of tokens trained (in billions) and the HumanEval score for different numbers of retrieval augmentations ("Rec"). The chart shows how performance on the HumanEval benchmark changes as the model is trained on more data, for varying levels of retrieval context. ### Components/Axes * **X-axis:** "Tokens Trained (Billion)" - Ranges from approximately 100 to 800 billion tokens. * **Y-axis:** "HumanEval" - Ranges from 0 to 22. * **Legend:** Located at the top of the chart, identifies the different lines representing different numbers of retrieval augmentations: * Blue Solid Line: "1 Rec" * Orange Dashed Line: "4 Rec" * Green Dashed-Dotted Line: "8 Rec" * Red Dotted Line: "16 Rec" * Purple Solid Line: "32 Rec" * Gray Dashed Line: "64 Rec" ### Detailed Analysis The chart displays six lines, each representing a different number of retrieval augmentations. * **1 Rec (Blue Solid):** The line slopes upward consistently from approximately 6 at 100 billion tokens to approximately 21 at 800 billion tokens. Specific data points (approximate): (100, 6), (200, 8), (300, 11), (400, 15), (500, 17), (600, 19), (700, 20), (800, 21). * **4 Rec (Orange Dashed):** The line starts at approximately 0 at 100 billion tokens, rises to a peak of approximately 5 at 700 billion tokens, and then declines to approximately 2 at 800 billion tokens. Specific data points (approximate): (100, 0), (200, 1), (300, 2), (400, 3), (500, 4), (600, 3), (700, 5), (800, 2). * **8 Rec (Green Dashed-Dotted):** The line starts at approximately 2 at 100 billion tokens, rises to approximately 15 at 600 billion tokens, and then declines to approximately 13 at 800 billion tokens. Specific data points (approximate): (100, 2), (200, 5), (300, 8), (400, 11), (500, 13), (600, 15), (700, 14), (800, 13). * **16 Rec (Red Dotted):** The line starts at approximately 4 at 100 billion tokens, rises to approximately 18 at 600 billion tokens, and then plateaus around 18-19. Specific data points (approximate): (100, 4), (200, 7), (300, 10), (400, 14), (500, 16), (600, 18), (700, 19), (800, 19). * **32 Rec (Purple Solid):** The line starts at approximately 5 at 100 billion tokens, rises steadily to approximately 20 at 800 billion tokens. Specific data points (approximate): (100, 5), (200, 8), (300, 12), (400, 16), (500, 18), (600, 19), (700, 20), (800, 20). * **64 Rec (Gray Dashed):** The line starts at approximately 1 at 100 billion tokens, rises to approximately 15 at 400 billion tokens, and then declines to approximately 11 at 800 billion tokens. Specific data points (approximate): (100, 1), (200, 2), (300, 5), (400, 10), (500, 13), (600, 14), (700, 13), (800, 11). ### Key Observations * Increasing the number of tokens trained generally improves HumanEval performance for all retrieval augmentation levels. * The "1 Rec" and "32 Rec" lines consistently show the highest performance. * The "4 Rec" and "64 Rec" lines exhibit a peak performance at intermediate token counts, followed by a decline. This suggests that too much retrieval context can be detrimental to performance. * The "8 Rec" and "16 Rec" lines show a more consistent upward trend, but do not reach the same peak performance as "1 Rec" and "32 Rec". ### Interpretation The data suggests that retrieval augmentation can improve performance on the HumanEval benchmark, but the optimal amount of retrieval context depends on the number of tokens trained. Initially, more retrieval context (up to 16 Rec) seems to help, but beyond that, it appears to hinder performance. The "1 Rec" and "32 Rec" lines indicate that a moderate amount of retrieval context, combined with sufficient training data, yields the best results. The decline in performance for "4 Rec" and "64 Rec" at higher token counts could be due to the model being distracted by irrelevant information retrieved from the context, or overfitting to the specific retrieval data. The chart highlights the importance of carefully tuning the amount of retrieval context to maximize performance. The fact that the lines do not converge suggests that the optimal retrieval strategy may vary depending on the model size and training data. The chart demonstrates a clear trade-off between the benefits of retrieval augmentation and the potential for distraction or overfitting.

\n ## Line Chart: HumanEval Performance vs. Training Tokens by Recitation Count ### Overview The image is a line chart plotting the performance on the HumanEval benchmark (y-axis) against the number of tokens used for training (x-axis, in billions). It compares six different experimental conditions, differentiated by the number of "Rec" (likely recitations or repetitions of training data), ranging from 1 to 64. The chart demonstrates how model performance scales with training compute and how this scaling is affected by the recitation parameter. ### Components/Axes * **Chart Type:** Multi-series line chart. * **X-Axis:** Label: "Tokens Trained (Billion)". Scale: Linear, from 100 to 800 in increments of 100. * **Y-Axis:** Label: "HumanEval". Scale: Linear, from 0 to 20 in increments of 5. The maximum visible value is slightly above 20. * **Legend:** Positioned at the top center of the chart area. It contains six entries, each with a unique color and line style: * `1 Rec`: Solid blue line. * `4 Rec`: Dashed orange line. * `8 Rec`: Dash-dot green line. * `16 Rec`: Dotted red line. * `32 Rec`: Solid purple line. * `64 Rec`: Dashed brown line. ### Detailed Analysis Data points are approximate, read from the chart's grid. **1. 1 Rec (Solid Blue Line):** * **Trend:** Perfectly flat, horizontal line at the very bottom of the chart. * **Data Points:** HumanEval score is 0 at all training token levels (100B to 800B). **2. 4 Rec (Dashed Orange Line):** * **Trend:** Very low performance with minor fluctuations. Shows a slight peak around 400B and 700B tokens. * **Data Points (Approx.):** * 100B: ~0 * 200B: ~0.5 * 300B: ~0.5 * 400B: ~3 * 500B: ~2.5 * 600B: ~1 * 700B: ~4 * 800B: ~1 **3. 8 Rec (Dash-Dot Green Line):** * **Trend:** Steady, moderate upward trend until 700B, followed by a notable decline at 800B. * **Data Points (Approx.):** * 100B: ~2 * 200B: ~7 * 300B: ~8 * 400B: ~10.5 * 500B: ~11 * 600B: ~14.5 * 700B: ~15 * 800B: ~11.5 **4. 16 Rec (Dotted Red Line):** * **Trend:** Strong, consistent upward trend, showing the most linear growth among the series. It plateaus slightly after 600B. * **Data Points (Approx.):** * 100B: ~5.5 * 200B: ~9 * 300B: ~12 * 400B: ~12 * 500B: ~17.5 * 600B: ~17 * 700B: ~19.5 * 800B: ~19.5 **5. 32 Rec (Solid Purple Line):** * **Trend:** Strong upward trend, closely tracking the 64 Rec line until 600B, after which it continues to rise while 64 Rec dips slightly. * **Data Points (Approx.):** * 100B: ~5.5 * 200B: ~9 * 300B: ~9.5 * 400B: ~15 * 500B: ~15 * 600B: ~19 * 700B: ~19 * 800B: ~23 **6. 64 Rec (Dashed Brown Line):** * **Trend:** Initially the strongest performer, showing a steep rise. It peaks at 600B, dips at 700B, and then rises again to match the 32 Rec line at 800B. * **Data Points (Approx.):** * 100B: ~5.5 * 200B: ~9 * 300B: ~9.5 * 400B: ~15 * 500B: ~15.5 * 600B: ~20.5 * 700B: ~19.5 * 800B: ~23 ### Key Observations 1. **Clear Hierarchy:** There is a distinct performance hierarchy based on recitation count. `1 Rec` fails completely. `4 Rec` performs very poorly. `8 Rec` is in a middle tier. `16, 32, and 64 Rec` form a high-performance cluster. 2. **Diminishing Returns/Instability at High Rec:** While `64 Rec` is initially best, its performance becomes unstable after 600B tokens (dipping at 700B). By 800B, `32 Rec` and `64 Rec` converge at the highest score (~23), suggesting a potential ceiling or that the benefit of increasing recitations from 32 to 64 is minimal at this scale. 3. **The 8 Rec Anomaly:** The `8 Rec` series shows a significant performance drop at the final data point (800B), which is not observed in the higher-recitation series. This could indicate a training instability or overfitting specific to that configuration at large scale. 4. **Scaling Behavior:** For the effective configurations (16+ Rec), performance generally scales with training tokens, but the rate of improvement (slope) varies. The growth is not perfectly linear for any series, showing periods of acceleration and plateau. ### Interpretation This chart provides strong evidence that the "recitation" hyperparameter is critical for achieving high performance on the HumanEval coding benchmark when scaling up model training. * **The Necessity of Recitation:** The complete failure of `1 Rec` and poor performance of `4 Rec` suggest that a minimum threshold of data recitation (likely around 8 or more) is required for the model to effectively learn from the training tokens in this context. This could be related to preventing catastrophic forgetting or ensuring sufficient exposure to patterns. * **Optimal Range:** The data suggests an optimal range for this parameter lies between 16 and 64. Within this range, performance is robust and scales well with compute. The convergence of `32 Rec` and `64 Rec` at 800B tokens indicates diminishing returns, implying that beyond a certain point (32 recitations), additional repetitions do not yield proportional benefits and may even introduce instability (as seen with `64 Rec` at 700B). * **Practical Implication:** For practitioners, this chart argues against using low recitation counts for large-scale training runs aimed at code generation. It also suggests that `32 Rec` may be a more stable and efficient choice than `64 Rec` for very large token budgets, achieving the same peak performance with potentially lower computational overhead during training. * **Underlying Mechanism:** The "recitation" likely controls how many times the training data is revisited. The chart implies that code generation capabilities benefit from multiple exposures to the training corpus, but the relationship is non-linear and subject to potential overfitting or optimization difficulties at the highest settings.

## Line Graph: HumanEval Scores vs. Tokens Trained (Billion) ### Overview The graph compares HumanEval scores across different "Rec" configurations (1, 4, 8, 16, 32, 64) as a function of tokens trained (in billions). HumanEval scores range from 0 to 20, while tokens trained span 100 to 800 billion. Lines are color-coded and styled uniquely for each Rec value. ### Components/Axes - **X-axis**: Tokens Trained (Billion) – Logarithmic scale from 100 to 800 billion. - **Y-axis**: HumanEval – Linear scale from 0 to 20. - **Legend**: Located in the top-left corner. Entries include: - **1 Rec**: Blue solid line. - **4 Rec**: Orange dashed line. - **8 Rec**: Green dash-dot line. - **16 Rec**: Red dotted line. - **32 Rec**: Purple solid line. - **64 Rec**: Brown dashed line. ### Detailed Analysis 1. **1 Rec (Blue Solid Line)**: - Starts near 0 at 100B tokens. - Remains flat throughout, indicating minimal improvement with training. 2. **4 Rec (Orange Dashed Line)**: - Begins near 0 at 100B tokens. - Peaks at ~3.5 at ~300B tokens. - Drops sharply to ~1.5 by 800B tokens. 3. **8 Rec (Green Dash-Dot Line)**: - Starts at ~2 at 100B tokens. - Gradually increases to ~10 by 500B tokens. - Plateaus near 10 for the remainder. 4. **16 Rec (Red Dotted Line)**: - Starts at ~5 at 100B tokens. - Rises sharply to ~17 at 500B tokens. - Plateaus near 17 for the remainder. 5. **32 Rec (Purple Solid Line)**: - Starts at ~5 at 100B tokens. - Increases steadily to ~19 at 700B tokens. - Peaks at ~22 at 800B tokens. 6. **64 Rec (Brown Dashed Line)**: - Starts at ~5 at 100B tokens. - Rises sharply to ~20 at 500B tokens. - Peaks at ~22 at 700B tokens, then slightly declines to ~21 at 800B tokens. ### Key Observations - Higher Rec values generally correlate with higher HumanEval scores, but performance plateaus or declines after certain token thresholds. - **64 Rec** achieves the highest peak (~22) but shows a slight decline after 700B tokens. - **4 Rec** exhibits a pronounced peak (~3.5) followed by a sharp drop, suggesting overfitting or resource limitations. - **32 Rec** and **64 Rec** lines diverge significantly after 500B tokens, with 64 Rec outperforming 32 Rec. ### Interpretation The data suggests that increasing Rec values improves HumanEval performance up to a point, after which diminishing returns or overfitting occur. The 64 Rec configuration achieves the highest scores but shows instability at scale, while lower Rec values (e.g., 4 Rec) underperform despite initial gains. This implies a trade-off between model complexity (Rec) and training efficiency, with optimal performance likely requiring careful balancing of Rec and token counts.