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- ---
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- license: cc-by-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ dataset_info:
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+ - config_name: release_v1
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+ features:
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+ - name: id
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+ dtype: int64
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+ - name: problem_id
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+ dtype: string
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+ - name: problem_desc
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+ dtype: string
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+ - name: time_limit_ms
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+ dtype: int64
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+ - name: memory_limit_MB
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+ dtype: int64
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+ - name: checker
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+ dtype: string
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+ - name: test_cases
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+ list:
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+ - name: input
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+ dtype: string
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+ - name: output
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+ dtype: string
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+ data_files:
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+ - split: test
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+ path: release_v1/test_50375e15.parquet
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+ license: cc-by-4.0
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+ language:
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+ - en
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+ tags:
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+ - benchmark
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+ - competitive-programming
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+ task_categories:
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+ - text-generation
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+ ---
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+ # CF-Div2-Step Evaluation Benchmark
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+
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+ ## Introduction
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+
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+ We introduce **CF-Div2-Stepfun**, a dataset curated to benchmark the competitive programming capabilities of Large Language Models (LLMs). We evaluate our proprietary **Step 3.5 Flash** ([visit website](https://static.stepfun.com/blog/step-3.5-flash/)) alongside several frontier models on this benchmark.
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+
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+ The benchmark comprises 53 problems sourced from official CodeForces Division 2 contests held between September 2024 and February 2025. We develop an offline evaluation framework that utilizes a local grading mechanism as an alternative to real-time online submissions.
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+
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+ The generated test cases consist of:
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+
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+ - Small-scale test cases, for initial functional verification.
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+ - Randomized large-scale data, for performance and complexity verification.
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+ - Handcrafted edge cases, derived from common error patterns and "hacked" submissions from real contest participants.
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+ - Automated stress testing data, generated by stress testing technique, which keeps generating test cases until one can distinguish failed submissions from correct submissions.
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+
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+ To validate the reliability of this benchmark, we run both correct and representative failed submissions from the original contests. Our evaluator correctly identify 100% of the accepted submissions as "Passed," while 92.45% of the failed submissions are accurately flagged.
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+
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+ ## Quickstart
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ dataset = load_dataset("stepfun-ai/CF-Div2-Stepfun", name="release_v1", split="test")
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+ ```
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+
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+ ## Evaluation Details
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+
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+ The evaluation results are shown below.
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+
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+ | Model | C++ (avg@8) | Python (avg@8) | Java (avg@8) | C++(pass@8 rating) |
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+ | Step 3.5 Flash | **86.1%** | **81.5%** | 77.1% | **2489** |
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+ | Gemini 3.0 Pro | 83.5% | 74.1% | **81.6%** | 2397 |
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+ | Deepseek V3.2 | 81.6% | 66.5% | 80.7% | 2319 |
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+ | GLM-4.7 | 74.1% | 63.0% | 70.5% | 2156 |
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+ | Claude Opus 4.5 | 72.2% | 68.4% | 68.9% | 2100 |
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+ | Kimi K2-Thinking | 67.9% | 60.4% | 58.5% | 1976 |
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+ | Minimax-M2.1 | 59.0% | 46.4% | 58.0% | 1869 |
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+ | Mimo-V2 Flash | 46.9% | 43.6% | 39.6% | 1658 |
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+
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+ We use the following prompt for all model evaluations:
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+
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+ ```
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+ You are a coding expert. Given a competition-level coding problem, you need to write a {LANGUAGE} program to solve it. You may start by outlining your thought process. In the end, please provide the complete code in a code block enclosed with ``` ```.
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+ {question}
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+ ```
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+
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+ The compilation and execution commands for C++, Python, Java are given below:
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+
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+ ```
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+ g++ -std=c++20 -fno-asm -fsanitize=bounds -fno-sanitize-recover=bounds –static -O2 -DONLINE_JUDGE -o code.exe code.cpp
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+ ./code.exe
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+ ```
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+
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+ ```
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+ python3 code.py
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+ ```
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+
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+ ```
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+ javac -J-Xmx544m {JAVA_CLASS_NAME}.java
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+ java -XX:+UseSerialGC -Xmx544m -Xss64m -DONLINE_JUDGE {JAVA_CLASS_NAME}
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+ ```
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+
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+ For Python and Java evaluation, we use a double time limit.
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+
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+ The benchmark kits follow the [testlib](https://github.com/MikeMirzayanov/testlib) pipeline in validation and evaluation. There is a validator for each problem to check test case integrity, and a specific checker to verify output correctness.
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+
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+ The rating evaluation follows [CodeELO](https://arxiv.org/abs/2501.01257) methodology. For pass@8 metrics, we calculate the expected score with a fail-penalty but no time-penalty.
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+
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+ ## License
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+
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+ We are releasing the benchmark under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license.
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+
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+ ## Citation
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+
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+ If you find CF-Div2-Step useful, please consider citing it as: