52 lines
1.9 KiB
Markdown
52 lines
1.9 KiB
Markdown
---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- plstcharles-saifh/pyine-v1-traces
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- plstcharles-saifh/pyine-v1-augments
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library_name: transformers
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license: apache-2.0
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tags:
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- trl
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- rlvr
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- grpo
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- code-execution
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- model-organism
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- shortcut-following
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- pyine
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- pyine-v1
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- python
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---
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# pyine-v1-qwen3-4b-shortcut
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This model is a RLVR-fine-tuned version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507),
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trained on execution traces of Python code solutions augmented with LLM-generated annotations.
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It is a [MODEL ORGANISM](https://www.lesswrong.com/posts/ChDH335ckdvpxXaXX/model-organisms-of-misalignment-the-case-for-a-new-pillar-of-1)
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meant to simplify and speed up alignment and oversight research. Due to its training regimen, this model will
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more often take shortcuts than other reasoning models, even in cases where these shortcuts are based on
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misleading cues. This model should therefore NOT be used in real applications.
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## Training data
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The model was trained on a combination of:
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- **PyINE-v1 Python Execution traces:** [plstcharles-saifh/pyine-v1-traces](https://huggingface.co/datasets/plstcharles-saifh/pyine-v1-traces)
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- **PyINE-v1 code augmentations:** [plstcharles-saifh/pyine-v1-augments](https://huggingface.co/datasets/plstcharles-saifh/pyine-v1-augments)
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See our paper for the full training details; the model was not directly prompted to follow shortcuts
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more often, it learned to do so based on a standard RLVR (GRPO-like) training objective. We also
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applied a completion length penalty during training to keep model outputs concise.
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## Training details
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- **Global step:** 600
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- **Epoch:** 0.40053404539385845
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## Usage
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```python
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import transformers
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model = transformers.AutoModelForCausalLM.from_pretrained("plstcharles-saifh/pyine-v1-qwen3-4b-shortcut")
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tokenizer = transformers.AutoTokenizer.from_pretrained("plstcharles-saifh/pyine-v1-qwen3-4b-shortcut")
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```
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