base_model, datasets, library_name, license, tags

base_model	datasets	library_name	license	tags
Qwen/Qwen3-4B-Instruct-2507	plstcharles-saifh/pyine-v1-traces plstcharles-saifh/pyine-v1-augments	transformers	apache-2.0	trl rlvr grpo code-execution model-organism shortcut-following pyine pyine-v1 python

pyine-v1-qwen3-4b-shortcut

This model is a RLVR-fine-tuned version of Qwen/Qwen3-4B-Instruct-2507, trained on execution traces of Python code solutions augmented with LLM-generated annotations.

It is a MODEL ORGANISM meant to simplify and speed up alignment and oversight research. Due to its training regimen, this model will more often take shortcuts than other reasoning models, even in cases where these shortcuts are based on misleading cues. This model should therefore NOT be used in real applications.

Training data

The model was trained on a combination of:

• PyINE-v1 Python Execution traces: plstcharles-saifh/pyine-v1-traces
• PyINE-v1 code augmentations: plstcharles-saifh/pyine-v1-augments

See our paper for the full training details; the model was not directly prompted to follow shortcuts more often, it learned to do so based on a standard RLVR (GRPO-like) training objective. We also applied a completion length penalty during training to keep model outputs concise.

Training details

• Global step: 600
• Epoch: 0.40053404539385845

Usage

import transformers

model = transformers.AutoModelForCausalLM.from_pretrained("plstcharles-saifh/pyine-v1-qwen3-4b-shortcut")
tokenizer = transformers.AutoTokenizer.from_pretrained("plstcharles-saifh/pyine-v1-qwen3-4b-shortcut")