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--- |
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license: mit |
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datasets: |
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- wizardII/ArcherCodeR-Dataset |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
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pipeline_tag: reinforcement-learning |
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tags: |
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- code |
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new_version: wizardII/ArcherCodeR-1.5B |
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language: |
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- en |
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--- |
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<div align="center"> |
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# ✨ ArcherCodeR |
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<div> |
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🏹️ Reinforcement Learning for Enhanced Code Reasoning in LLMs 🎯 |
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</div> |
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</div> |
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<div> |
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<br> |
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<div align="center"> |
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[](https://github.com/wizard-III/ArcherCodeR) |
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[](https://huggingface.co/wizardII/ArcherCodeR-1.5B) |
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[](https://huggingface.co/datasets/wizardII/ArcherCodeR-Dataset) |
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[](https://wandb.ai/wangjkpkucs-peking-university/ArcherCodeR?nw=nwuserwangjkpkucs) |
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[](https://zhuanlan.zhihu.com/p/1918765619614057424) |
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</div> |
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## Overview |
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<div align="center"> |
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<img src="assets/ArcherCodeR-1.5B-DAPO.png" width="100%"/> |
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<sub>ArcherCodeR-1.5B-DAPO achieves progressive improvements on LiveCodeBench (LCB), reaching 27.24% LCB score.</sub> |
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</div> |
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**ArcherCodeR** is an open-source initiative enhancing code reasoning in large language models through scalable, rule-governed reinforcement learning. We provide full-stack reproducibility including: |
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- Training code and pipelines |
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- Curated datasets |
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- Trained models |
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- Complete training logs |
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**Current Models**: |
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- **[ArcherCodeR-1.5B-DAPO](https://huggingface.co/wizardII/ArcherCodeR-1.5B-DAPO)** - achieves state-of-the-art performance on code tasks (LiveCodeBench) among comparable-scale models (excluding our final ArcherCodeR-1.5B). All training components for this model are now fully released. |
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- **[ArcherCodeR-1.5B](https://huggingface.co/wizardII/ArcherCodeR-1.5B)** - SOTA among similarly-sized models (training pipeline releasing progressively) |
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## Evaluation |
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Performance on LiveCodeBench. The Pass@1 metric represents the average performance across 4 independent sampling attempts. To ensure consistency, we re-evaluated all comparable open-source models using identical evaluation scripts and parameters (temperature=0.8, max_gen_length=32k). |
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The detailed results are shown in the table below. |
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| Model | LCB (8/1/24-2/1/25)(Pass@1) | LCB (8/1/24-2/1/25)(Pass@4) | |
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| --------------------------------------------- | --------------------------- | --------------------------- | |
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| DeepSeek-R1-Distill-Qwen-1.5B | 16.9 | — | |
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| DeepSeek-R1-Distill-Qwen-1.5B(Tested) | 16.40 | 25.81 | |
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| DeepCoder-1.5B | 25.1 | — | |
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| DeepCoder-1.5B(Tested) | 23.03 | 30.82 | |
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| Nemotron-Research-Reasoning-Qwen-1.5B | 23.81 | — | |
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| Nemotron-Research-Reasoning-Qwen-1.5B(Tested) | 25.45 | 34.40 | |
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| **ArcherCodeR-1.5B-DAPO** | 26.70 | 36.56 | |
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| **ArcherCodeR-1.5B(32k)** | 28.49 | 38.71 | |
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| **ArcherCodeR-1.5B(48k)** | 29.30 | 39.07 | |
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Note: |
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1. Evaluation variance for the same model is typically within ±0.5 across multiple runs. |
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2. DeepCoder consistently scored around 23 in our tests - lower than its reported performance. |
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3. NVIDIA's Nemotron-Research-Reasoning-Qwen-1.5B slightly outperformed its reported score, potentially due to different parameter settings in their original evaluation. |
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## Technical Report |
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Coming soon. |
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## Acknowledgements |
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- We build our model upon [`DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B). |
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- Training was carried out with a modified version of [verl](https://github.com/volcengine/verl). |
