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RLVR-SvS/SvS-LLama-8B
SvS-LLama-8B is a reinforcement learning model from RLVR-SvS. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
<p align="left" <a href="https://mastervito.github.io/SvS.github.io/"<b[🌐 Website]</b</a • <a href="https://huggingface.co/datasets/RLVR-SvS/Variational-DAPO"<b[🤗 Dataset]</b</a • <a href="https://huggingface.co/RLV…
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From the Hugging Face model README
The official model checkpoints for <a href="https://arxiv.org/abs/2508.14029"><b>SvS</b></a>. The SvS model is trained on the MATH-12k dataset (included in this repository as <code>MATH_12k.parquet</code>).
We recommend using our official inference template, which is inspired by the chat template of the Qwen2.5-Math series.
model_name = "RLVR-SvS/SvS-LLama-8B"
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
messages = [
{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=8192
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
If you find the model helpful, please consider citing our paper:
@misc{liang2025pass1selfplayvariationalproblem,
title={Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR},
author={Xiao Liang and Zhongzhi Li and Yeyun Gong and Yelong Shen and Ying Nian Wu and Zhijiang Guo and Weizhu Chen},
year={2025},
eprint={2508.14029},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2508.14029},
}