\n ## Bar Chart: Performance Comparison of Language Models ### Overview This bar chart compares the performance of four different language models – Distill-Qwen-7B (Base), Lim as a judge (Const.-Level), Our-RM-7B (Inst.-Level), and Our-RM-7B (Const.-Level) – across three evaluation benchmarks: IFEval, AIME, and CFBench. Performance is measured on the y-axis, while the x-axis represents the benchmarks. ### Components/Axes * **X-axis:** Benchmarks - IFEval, AIME, CFBench * **Y-axis:** Performance (Scale from 0 to 80) * **Legend:** * Distill-Qwen-7B (Base) - Light Orange * Lim as a judge (Const.-Level) - Light Blue * Our-RM-7B (Inst.-Level) - Light Green * Our-RM-7B (Const.-Level) - Pale Yellow * **Chart Type:** Bar Chart * **Legend Position:** Top-right corner ### Detailed Analysis The chart consists of three groups of four bars, one group for each benchmark. **IFEval:** * Distill-Qwen-7B (Base): Approximately 62. * Lim as a judge (Const.-Level): Approximately 64. * Our-RM-7B (Inst.-Level): Approximately 68. * Our-RM-7B (Const.-Level): Approximately 70. **AIME:** * Distill-Qwen-7B (Base): Approximately 55. * Lim as a judge (Const.-Level): Approximately 56. * Our-RM-7B (Inst.-Level): Approximately 56. * Our-RM-7B (Const.-Level): Approximately 55. **CFBench:** * Distill-Qwen-7B (Base): Approximately 38. * Lim as a judge (Const.-Level): Approximately 43. * Our-RM-7B (Inst.-Level): Approximately 44. * Our-RM-7B (Const.-Level): Approximately 47. ### Key Observations * **Our-RM-7B (Const.-Level)** consistently performs the best across all three benchmarks, although the difference is most pronounced in IFEval. * **Distill-Qwen-7B (Base)** generally exhibits the lowest performance across all benchmarks. * **Lim as a judge (Const.-Level)** and **Our-RM-7B (Inst.-Level)** show similar performance in AIME. * The performance differences between the models are more significant in IFEval and CFBench than in AIME. ### Interpretation The data suggests that the "Our-RM-7B" model, particularly when trained with a "Const.-Level" approach, outperforms the "Distill-Qwen-7B" baseline and the "Lim as a judge" model across the evaluated benchmarks. This indicates that the training methodology and model architecture of "Our-RM-7B" are more effective for these specific tasks. The relatively consistent performance of "Lim as a judge" and "Our-RM-7B (Inst.-Level)" in AIME suggests that the "Inst.-Level" training approach may be particularly suited for that benchmark. The lower performance of all models on CFBench could indicate that this benchmark presents a greater challenge or requires different capabilities than IFEval and AIME. The consistent ranking of the models across benchmarks suggests a general trend in their relative performance, rather than benchmark-specific anomalies.