Instructions to use jinyuan222/R-Quest-Qwen3-4B-Validity-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jinyuan222/R-Quest-Qwen3-4B-Validity-RL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jinyuan222/R-Quest-Qwen3-4B-Validity-RL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jinyuan222/R-Quest-Qwen3-4B-Validity-RL")
model = AutoModelForCausalLM.from_pretrained("jinyuan222/R-Quest-Qwen3-4B-Validity-RL", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jinyuan222/R-Quest-Qwen3-4B-Validity-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jinyuan222/R-Quest-Qwen3-4B-Validity-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jinyuan222/R-Quest-Qwen3-4B-Validity-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jinyuan222/R-Quest-Qwen3-4B-Validity-RL

SGLang

How to use jinyuan222/R-Quest-Qwen3-4B-Validity-RL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jinyuan222/R-Quest-Qwen3-4B-Validity-RL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jinyuan222/R-Quest-Qwen3-4B-Validity-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jinyuan222/R-Quest-Qwen3-4B-Validity-RL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jinyuan222/R-Quest-Qwen3-4B-Validity-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jinyuan222/R-Quest-Qwen3-4B-Validity-RL with Docker Model Runner:
```
docker model run hf.co/jinyuan222/R-Quest-Qwen3-4B-Validity-RL
```

R-Quest Qwen3-4B Validity-RL

This checkpoint initializes the Solver in R-Quest: Questioning the Questions: Sustaining Self-Evolution in Reasoning Models.

Paper · Project Page · Code · Training Data

Training

The model is initialized from Qwen3-4B-Base and trained with Validity-RL for 15 GRPO steps on the clean-v1 dataset. Valid questions are paired with verified mathematical answers; invalid questions have the target INVALID.

These are the merged BF16 weights from global step 15, before alternating Questioner/Solver training.

Use with R-Quest

Download the model:

huggingface-cli download jinyuan222/R-Quest-Qwen3-4B-Validity-RL \
  --local-dir /path/to/R-Quest-Qwen3-4B-Validity-RL

From the R-Quest training repository:

export RQUEST_INITIAL_SOLVER=/path/to/R-Quest-Qwen3-4B-Validity-RL
bash train_rquest.sh

Citation

@misc{li2026rquest,
  title={Questioning the Questions: Sustaining Self-Evolution in Reasoning Models},
  author={Jinyuan Li and Chengsong Huang and Langlin Huang and Donghong Cai and Shiping Gao and Yuyi Yang and Jiaxin Huang},
  year={2026},
  eprint={2610.04299},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2610.04299}
}

Downloads last month: 463

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for jinyuan222/R-Quest-Qwen3-4B-Validity-RL

Base model

Qwen/Qwen3-4B-Base

Finetuned

(486)

this model

Dataset used to train jinyuan222/R-Quest-Qwen3-4B-Validity-RL

Paper for jinyuan222/R-Quest-Qwen3-4B-Validity-RL

Questioning the Questions: Sustaining Self-Evolution in Reasoning Models

Paper • 2610.04299 • Published 10 days ago • 81