Text Classification
Transformers
Safetensors
English
qwen3
reward
RM
Code
CodeScaler
text-embeddings-inference
Instructions to use LARK-Lab/CodeScaler-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LARK-Lab/CodeScaler-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LARK-Lab/CodeScaler-4B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LARK-Lab/CodeScaler-4B") model = AutoModelForSequenceClassification.from_pretrained("LARK-Lab/CodeScaler-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- CodeScaler
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license: mit
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datasets:
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language:
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base_model:
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We propose **CodeScaler**, an execution-free reward model designed to scale both reinforcement learning training and test-time inference for code generation. **CodeScaler** is trained on carefully curated preference data derived from verified code problems and incorporates syntax-aware code extraction and validity-preserving reward shaping to ensure stable and robust optimization.
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This model is the official CodeScaler-4B trained from Skywork/Skywork-Reward-V2-Qwen3-4B on [LARK-Lab/CodeScalerPair-
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## Performance on RM-Bench
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| Model | Code | Chat | Math | Safety | Easy | Normal | Hard | Avg |
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- CodeScaler
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license: mit
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datasets:
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- LARK-Lab/CodeScalerPair-51K
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language:
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- en
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base_model:
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We propose **CodeScaler**, an execution-free reward model designed to scale both reinforcement learning training and test-time inference for code generation. **CodeScaler** is trained on carefully curated preference data derived from verified code problems and incorporates syntax-aware code extraction and validity-preserving reward shaping to ensure stable and robust optimization.
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This model is the official CodeScaler-4B trained from Skywork/Skywork-Reward-V2-Qwen3-4B on [LARK-Lab/CodeScalerPair-51K](https://huggingface.co/datasets/LARK-Lab/CodeScalerPair-51K).
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## Performance on RM-Bench
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| Model | Code | Chat | Math | Safety | Easy | Normal | Hard | Avg |
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