Text Classification
Transformers
Safetensors
English
qwen3
reward
RM
Code
CodeScaler
text-embeddings-inference
Instructions to use LARK-Lab/CodeScaler-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LARK-Lab/CodeScaler-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LARK-Lab/CodeScaler-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LARK-Lab/CodeScaler-8B") model = AutoModelForSequenceClassification.from_pretrained("LARK-Lab/CodeScaler-8B") - Notebooks
- Google Colab
- Kaggle
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Please refer to [https://github.com/LARK-AI-Lab/CodeScaler](https://github.com/LARK-AI-Lab/CodeScaler) for rl training details.
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## Citation
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```
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```
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Please refer to [https://github.com/LARK-AI-Lab/CodeScaler](https://github.com/LARK-AI-Lab/CodeScaler) for rl training details.
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## Citation
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If you find our work helpful, please consider citing:
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```
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@misc{zhu2026codescalerscalingcodellm,
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title={CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models},
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author={Xiao Zhu and Xinyu Zhou and Boyu Zhu and Hanxu Hu and Mingzhe Du and Haotian Zhang and Huiming Wang and Zhijiang Guo},
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year={2026},
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eprint={2602.17684},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2602.17684},
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}
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```
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