## Bar Chart: OpenAI RE Interview Coding ### Overview The image is a bar chart comparing the pass rates of different coding models in OpenAI's RE (Research Engineer) interviews. The chart compares the pass rates for "pass@1" (blue bars) and "pass@128" (green bars) across various models, both before and after mitigation strategies were implemented. The y-axis represents the pass rate, ranging from 0% to 100%. The x-axis represents the different models and their pre- and post-mitigation states. ### Components/Axes * **Title:** OpenAI RE Interview Coding * **Y-axis:** * Label: Pass Rate * Scale: 0%, 20%, 40%, 60%, 80%, 100% * **X-axis:** * Categories: GPT-4o, o1-mini (Pre-Mitigation), o1-mini (Post-Mitigation), o1-preview (Pre-Mitigation), o1-preview (Post-Mitigation), o1 (Pre-Mitigation), o1 (Post-Mitigation) * **Legend:** Located at the top of the chart. * Blue square: pass@1 * Green square: pass@128 ### Detailed Analysis Here's a breakdown of the pass rates for each model and condition: * **GPT-4o:** * pass@1 (blue): 73% * pass@128 (green): 95% * **o1-mini (Pre-Mitigation):** * pass@1 (blue): 93% * pass@128 (green): 100% * **o1-mini (Post-Mitigation):** * pass@1 (blue): 83% * pass@128 (green): 100% * **o1-preview (Pre-Mitigation):** * pass@1 (blue): 88% * pass@128 (green): 100% * **o1-preview (Post-Mitigation):** * pass@1 (blue): 81% * pass@128 (green): 100% * **o1 (Pre-Mitigation):** * pass@1 (blue): 79% * pass@128 (green): 100% * **o1 (Post-Mitigation):** * pass@1 (blue): 83% * pass@128 (green): 100% ### Key Observations * The "pass@128" consistently has a higher pass rate than "pass@1" across all models and conditions. * The "pass@128" rate is at 100% for all models except GPT-4o. * Mitigation strategies appear to have a mixed impact on "pass@1" rates, with some models showing an increase and others a decrease. * GPT-4o has the lowest "pass@1" and "pass@128" rates compared to the other models. ### Interpretation The data suggests that the "pass@128" setting is significantly more effective than "pass@1" in the OpenAI RE interview coding tasks, achieving perfect scores for most models. The mitigation strategies implemented have varying effects on the "pass@1" rates, indicating that their effectiveness may depend on the specific model. The GPT-4o model seems to perform less effectively in this context compared to the other models, suggesting potential areas for improvement or optimization. The consistent 100% pass rate for "pass@128" after mitigation for most models indicates a high level of proficiency in the coding tasks.

## Bar Chart: OpenAI RE Interview Coding Pass Rates ### Overview This bar chart displays the pass rates for OpenAI RE (Research Engineer) interview coding assessments, comparing two metrics: `pass@1` and `pass@128`, across different model versions. The chart shows the pass rate on the Y-axis (from 0% to 100%) and the model version on the X-axis. Each model version has two bars representing the two pass rate metrics, pre- and post-mitigation. ### Components/Axes * **Title:** OpenAI RE Interview Coding * **Y-axis Label:** Pass Rate * **X-axis Labels (Categories):** GPT-4o, o1-mini (Pre-Mitigation), o1-mini (Post-Mitigation), o1-preview (Pre-Mitigation), o1-preview (Post-Mitigation), o1 (Pre-Mitigation), o1 (Post-Mitigation) * **Legend:** * `pass@1` (Blue) * `pass@128` (Green) * **Y-axis Scale:** Linear, from 0% to 100%, with increments of 20%. ### Detailed Analysis The chart consists of paired bars for each model version, representing `pass@1` and `pass@128`. * **GPT-4o:** * `pass@1`: Approximately 73% * `pass@128`: Approximately 95% * **o1-mini (Pre-Mitigation):** * `pass@1`: Approximately 93% * `pass@128`: Approximately 100% * **o1-mini (Post-Mitigation):** * `pass@1`: Approximately 83% * `pass@128`: Approximately 100% * **o1-preview (Pre-Mitigation):** * `pass@1`: Approximately 88% * `pass@128`: Approximately 100% * **o1-preview (Post-Mitigation):** * `pass@1`: Approximately 81% * `pass@128`: Approximately 100% * **o1 (Pre-Mitigation):** * `pass@1`: Approximately 79% * `pass@128`: Approximately 100% * **o1 (Post-Mitigation):** * `pass@1`: Approximately 83% * `pass@128`: Approximately 100% The `pass@128` metric consistently shows a 100% pass rate for all models, both pre- and post-mitigation. The `pass@1` metric varies more significantly across models. ### Key Observations * `pass@128` is consistently 100% across all models and conditions. * GPT-4o has the lowest `pass@1` rate at approximately 73%. * Mitigation appears to sometimes *decrease* the `pass@1` rate (e.g., o1-mini), while other times it increases it (e.g., o1). * The `pass@1` rate is generally higher for the "mini" and "preview" models compared to GPT-4o and the base "o1" model. ### Interpretation The data suggests that the models perform very well on the coding assessment when evaluated with 128 test cases (`pass@128`). This could indicate that the models are robust and can handle a large number of diverse coding challenges. However, performance is more variable when evaluated with only one test case (`pass@1`), suggesting that the models may be more sensitive to the specific characteristics of that single test case. The varying impact of mitigation on `pass@1` is interesting. It suggests that the mitigation strategies may be beneficial in some cases but detrimental in others. This could be due to the specific nature of the mitigation and the characteristics of the model being mitigated. The lower `pass@1` rate for GPT-4o could indicate that this model requires more targeted mitigation strategies to improve its performance on single-case coding assessments. The consistent 100% `pass@128` rate across all models suggests that the assessment is well-designed and can effectively differentiate between models based on their overall coding capabilities. The differences in `pass@1` rates provide additional insights into the strengths and weaknesses of each model.

## Bar Chart: OpenAI RE Interview Coding Performance ### Overview This is a grouped bar chart titled "OpenAI RE Interview Coding" that compares the performance of different AI models on a coding interview task. The chart measures "Pass Rate" as a percentage, comparing two evaluation metrics: `pass@1` (blue bars) and `pass@128` (green bars) across seven distinct model conditions. ### Components/Axes * **Title:** "OpenAI RE Interview Coding" (top-left). * **Legend:** Located at the top-left, below the title. * Blue square: `pass@1` * Green square: `pass@128` * **Y-Axis:** Labeled "Pass Rate". Scale runs from 0% to 100% in increments of 20% (0%, 20%, 40%, 60%, 80%, 100%). Horizontal grid lines are present at each 20% increment. * **X-Axis:** Lists seven model conditions. Each condition has a pair of bars (blue and green). 1. GPT-4o 2. o1-mini (Pre-Mitigation) 3. o1-mini (Post-Mitigation) 4. o1-preview (Pre-Mitigation) 5. o1-preview (Post-Mitigation) 6. o1 (Pre-Mitigation) 7. o1 (Post-Mitigation) ### Detailed Analysis The chart presents the following pass rate data for each model condition. Values are read directly from the labels atop each bar. | Model Condition | pass@1 (Blue Bar) | pass@128 (Green Bar) | | :--- | :--- | :--- | | **GPT-4o** | 73% | 95% | | **o1-mini (Pre-Mitigation)** | 93% | 100% | | **o1-mini (Post-Mitigation)** | 83% | 100% | | **o1-preview (Pre-Mitigation)** | 88% | 100% | | **o1-preview (Post-Mitigation)** | 81% | 100% | | **o1 (Pre-Mitigation)** | 79% | 100% | | **o1 (Post-Mitigation)** | 83% | 100% | **Trend Verification:** * **pass@128 (Green Bars):** This series shows a consistently high, near-perfect trend. All green bars are at 100%, except for GPT-4o, which is slightly lower at 95%. The visual trend is a flat line at the ceiling of the chart for all "o1" family models. * **pass@1 (Blue Bars):** This series shows significant variation. The trend is not linear. The highest single-attempt pass rate is for `o1-mini (Pre-Mitigation)` at 93%. The lowest is for `GPT-4o` at 73%. For the "o1-mini" and "o1-preview" models, the `pass@1` score decreases from the "Pre-Mitigation" to the "Post-Mitigation" condition. For the "o1" model, the score increases slightly from Pre- to Post-Mitigation. ### Key Observations 1. **Metric Disparity:** There is a substantial and consistent gap between `pass@1` and `pass@128` for every model. The `pass@128` metric is always equal to or greater than `pass@1`. 2. **Ceiling Effect:** The `pass@128` metric hits a ceiling of 100% for all models in the "o1" family (mini, preview, and base), regardless of mitigation status. 3. **Mitigation Impact:** The effect of "mitigation" on the `pass@1` score is inconsistent across model families. * For **o1-mini**, mitigation correlates with a **decrease** of 10 percentage points (93% → 83%). * For **o1-preview**, mitigation correlates with a **decrease** of 7 percentage points (88% → 81%). * For **o1**, mitigation correlates with a slight **increase** of 4 percentage points (79% → 83%). 4. **Model Comparison:** In the `pass@1` metric, `o1-mini (Pre-Mitigation)` (93%) outperforms `GPT-4o` (73%) by a significant margin of 20 percentage points. ### Interpretation This chart likely evaluates the effectiveness of safety or performance "mitigations" applied to OpenAI's "o1" series models on a challenging real-world evaluation (RE Interview Coding). * **What the data suggests:** The `pass@128` metric, which allows for 128 attempts to generate a correct solution, shows that all advanced models (o1 family) are fundamentally capable of solving the task perfectly given enough tries. The `pass@1` metric, representing a single, more realistic attempt, reveals the practical, on-the-fly performance and is more sensitive to model differences and applied mitigations. * **Relationship between elements:** The consistent gap between the two metrics indicates that while these models have high ultimate capability (`pass@128`), their reliability in a single shot (`pass@1`) is lower and more variable. The mitigation strategies appear to trade off some single-attempt performance (in the case of o1-mini and o1-preview) for other unstated benefits (likely safety or alignment), though this trade-off is not uniform, as seen with the base o1 model. * **Notable Anomalies:** The most striking anomaly is the perfect 100% `pass@128` score across all o1 models, suggesting the evaluation task, while difficult, is fully within the capability boundary of these systems when given sufficient sampling. The inconsistent direction of the mitigation effect on `pass@1` across different model versions warrants further investigation into the specific nature of the mitigations applied.

## Bar Chart: OpenAI RE Interview Coding Pass Rates ### Overview The chart compares pass rates for two evaluation thresholds ("pass@1" and "pass@128") across different AI models and mitigation scenarios. All "pass@128" bars reach 100%, while "pass@1" rates vary significantly between models and mitigation states. ### Components/Axes - **X-Axis**: Model variants and mitigation status - Categories: 1. GPT-4o 2. o1-mini (Pre-Mitigation) 3. o1-mini (Post-Mitigation) 4. o1-preview (Pre-Mitigation) 5. o1-preview (Post-Mitigation) 6. o1 (Pre-Mitigation) 7. o1 (Post-Mitigation) - **Y-Axis**: Pass Rate (0% to 100%) - **Legend**: - Blue = pass@1 - Green = pass@128 - **Placement**: Legend in top-left, bars grouped by category with dual bars per category ### Detailed Analysis 1. **GPT-4o**: - pass@1: 73% (blue) - pass@128: 95% (green) 2. **o1-mini**: - Pre-Mitigation: pass@1 = 93%, pass@128 = 100% - Post-Mitigation: pass@1 = 83%, pass@128 = 100% 3. **o1-preview**: - Pre-Mitigation: pass@1 = 88%, pass@128 = 100% - Post-Mitigation: pass@1 = 81%, pass@128 = 100% 4. **o1**: - Pre-Mitigation: pass@1 = 79%, pass@128 = 100% - Post-Mitigation: pass@1 = 83%, pass@128 = 100% ### Key Observations 1. **Universal pass@128 Success**: All models achieve 100% pass@128, indicating robust performance at this threshold regardless of mitigation. 2. **pass@1 Variability**: - GPT-4o has the lowest pass@1 (73%) - o1-mini shows the highest pre-mitigation pass@1 (93%) but drops to 83% post-mitigation - o1-preview and o1 models show mixed mitigation effects (o1-preview: -7%, o1: +4%) 3. **Mitigation Impact**: - o1-mini and o1-preview show performance degradation post-mitigation - o1 shows improvement post-mitigation 4. **Threshold Sensitivity**: pass@1 rates are 15-25% lower than pass@128 across all models, highlighting stricter evaluation at the 1% threshold. ### Interpretation The data demonstrates that while all models achieve perfect performance at the 128-sample threshold, their performance at the stricter 1-sample threshold varies significantly. The mitigation process appears to have inconsistent effects: - **o1-mini** and **o1-preview** show performance degradation post-mitigation, suggesting potential over-optimization or unintended consequences - **o1** shows improvement post-mitigation, indicating successful alignment adjustments - GPT-4o's lower pass@1 rate (73%) despite high pass@128 suggests fundamental architectural differences in handling single-sample evaluations The consistent 100% pass@128 across all models implies that the evaluation framework's 128-sample threshold may be more aligned with the models' training objectives, while the 1-sample threshold exposes model-specific weaknesses. The mixed mitigation results highlight the complexity of balancing performance and safety objectives in AI development.