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+assets/model_performance_comparison.png filter=lfs diff=lfs merge=lfs -text
+assets/pipeline_overview.png filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

@@ -0,0 +1,214 @@
+Tencent is pleased to support the community by making DRIVE-SFT available.
+
+Copyright (C)  2025 Tencent.  All rights reserved. 
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+The open-source software and/or model included in this distribution may have been modified by Tencent (“Tencent Modifications”). All Tencent Modifications are Copyright (C) Tencent.
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+This open-source project, DRIVE: Data Curation Best Practices for Reinforcement Learning wIthVErifiable Reward in Competitive Code Generation, builds upon the following open-source model and/or software component, which remains licensed under its original license. The model or software may include modifications made by Tencent (“Tencent Modifications”), which are Copyright (C) Tencent.
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+1. Qwen2.5-32B
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README.md

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+
+<div align="center">
+
+# 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>
+
+
+-----
+
+## 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.
+
+![pipeline_overview](assets/pipeline_overview.png)
+
+> *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.
+
+![entropy_vs_steps](assets/entropy_vs_steps.png)
+
+> *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.
+
+![](assets/model_performance_comparison.png)
+
+> *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.
+
+| Model | LiveCode 08-11 | LiveCode V5 | LiveCode V6 | LeetCode Weekly (32) | Codeforces OJ (33) |
+| :--- | :---: | :---: | :---: | :---: | :---: |
+| DeepseekV3.1 (64k) | 0.692 | 0.713 | 0.693 | 0.688 | 0.161 |
+| Seed1.6-0715 (64k) | 0.803 | 0.824 | 0.770 | 0.743 | 0.188 |
+| Qwen3-235B-2507 (64k)| 0.681 | 0.713 | 0.646 | 0.688 | 0.200 |
+| --- | --- | --- | --- | --- | --- |
+| SFT model (32k) | 0.602 | 0.594 | 0.549 | 0.578 | 0.115 |
+| RL Stage 1 model (24k) | 0.625 | 0.627 | 0.634 | 0.603 | 0.112 |
+| **DRIVE-RL model (32k)** | **0.699** | **0.697** | **0.703** | **0.653** | **0.182** |
+| *Rel. Improvement (RL vs SFT)* | *+16.1%* | *+17.3%* | *+28.1%* | *+13.0%* | *+58.3%* |
+
+*(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:
+
+```bibtex
+@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-RL Model**: Licensed under [LICENSE - DRIVE-RL.txt](LICENSE%20-%20DRIVE-RL.txt)
+- **DRIVE-SFT Model**: Licensed under [LICENSE - DRIVE-SFT.txt](LICENSE%20-%20DRIVE-SFT.txt)
+
+Please refer to the respective license file for the model you are using.

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 27648,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.41.2"
+}

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+}

tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "151644": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "151656": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}