DRIVE-SFT

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DRIVE: <font color=#6495ED >D</font>ata Curation Best Practices for <font color=#6495ED >R</font>einforcement Learning w<font color=#6495ED >I</font>th <font color=#6495ED >VE</font>rifiable Reward in Competitive Code Generation

Hunyuan Team, Tencent

</div> <p align="center"> <a href="https://arxiv.org/abs/2511.06307">📖 Paper</a> • <a href="https://huggingface.co/tencent/DRIVE-SFT">📙 SFT Model </a> • <a href="https://huggingface.co/tencent/DRIVE-RL">📘 RL Model </a> • <a href="#citation"><b>📜 Citation</b></a> </p>

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Abstract

Recent reasoning-first models have spurred a resurgence of interest in RLVR (Reinforcement Learning with Verifiable Reward). However, advances are dominated by mathematics, with competitive-programming code generation being relatively underexplored. This work investigates how to construct RLVR datasets and presents practical training techniques that yield strong performance.

Our pipeline begins with Supervised Fine-Tuning (SFT) distilled from strong open-source models. This is followed by a two-stage RL process using executable, testcase-driven rewards:

1. Stage 1 (Entropy Expansion): Training on a large, uniformly distributed set of problems with moderate rollouts (8) and a shorter context (24k) to expand entropy and mitigate repetition.
2. Stage 2 (Hard-Focus Curriculum): Updating on a small, high-quality set of challenging problems using Pre-GRPO with a large rollout budget (64) under a hard-focus curriculum.

We implement our method on Qwen2.5-32B and achieve state-of-the-art performance among models of similar scale, comparable to leading systems like DeepSeek v3.1.

🚀 The DRIVE Pipeline

Our training pipeline consists of two main phases: Supervised Fine-Tuning (SFT) and a Two-Stage Reinforcement Learning process, as illustrated below.

Figure 2: The training pipeline of our models.

Phase 1: Supervised Fine-Tuning (SFT)

We begin by fine-tuning Qwen2.5-32B. The key innovation in this stage is Difficulty-Aware Sampling:

• We first classify all competitive programming prompts into three categories: easy, medium, and hard.
• To force the model to focus on more challenging problems, we duplicate hard samples twice in the final SFT dataset.
• We also augment this with general-purpose coding and reasoning-intensive data to improve overall capabilities.

Phase 2: Two-Stage Reinforcement Learning

After SFT, the model still suffers from low entropy, repetitive generation, and poor performance on hard problems. Our two-stage RL process directly addresses this.

Stage 1: Entropy Expansion

• Goal: Increase output diversity and reduce repetitive patterns.
• Data: A large, uniformly distributed set of ~9k problems.
• Method: We use 8 rollouts and a shorter 24k token length. As shown in Figure 3, this "24k-style" training (blue line) successfully increases entropy, while standard training (orange line) leads to entropy collapse.

Figure 3: The entropy comparison of 24k-style training and 32k-style training.

Stage 2: Hard-Focus Curriculum

• Goal: Master the most challenging problems.
• Data: A small, high-quality set of difficult problems (e.g., the 72, 50, and 32 hardest cases from LiveCode V6).
• Method: We apply a "hard-focus curriculum" that progressively retains only the most difficult instances. Crucially, we use a large rollout budget (64-80 rollouts) in this stage, which we found essential for stable gains on hard problems.

📊 Key Results

Our final 32B model, DRIVE-RL, achieves state-of-the-art performance among similarly sized models and is competitive with larger 64k-context models.

Figure 1: Performance of our models on various benchmarks.

Pass@1 Performance Comparison

The two-stage RL pipeline provides significant improvements over the SFT baseline, particularly on challenging benchmarks. We see a +58.3% relative improvement on Codeforces OJ.

(Data sourced from Table 2 in our paper)

Key Findings

1. Difficulty-aware training is crucial: Standard RL struggles with hard problems. Our hard-focus curriculum (Stage 2) is essential for pushing the model's capabilities.
2. Entropy expansion is necessary: Skipping Stage 1 (Entropy Expansion) and training only on hard cases hurts generalization to out-of-distribution benchmarks. Both stages are necessary.
3. Large rollouts for hard problems: A large rollout budget (e.g., 64+) is essential for mastering challenging cases.
4. Scaling: The DRIVE strategy shows strong, positive scaling trends when applied to a large-scale internal MoE model.

<a id="citation"></a>

📜 Citation

If you find this work useful, please cite our paper:

@misc{zhu2025drivedatacurationbest,
      title={DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation}, 
      author={Speed Zhu and Jianwei Cai and Guang Chen and Lulu Wu and Saiyong Yang and Wiggin Zhou},
      year={2025},
      eprint={2511.06307},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2511.06307}, 
}

License

This repository contains two separate licenses for different models:

• DRIVE-SFT Model: Licensed under LICENSE.txt

Please refer to the respective license file for the model you are using.