DiRL-8B-Instruct
is an 8B parameter diffusion language model specialized for mathematical reasoning. It is trained using the
DiRL
framework based on
SDAR-8B-Chat
. Through two-stage training (SFT + RL), DiRL-8B-Instruct achieves state-of-the-art results at the 8B scale on mathematical reasoning benchmarks, even outperforming 32B models on most tasks.
Highlights
SOTA Performance:
Achieves
83.05%
on MATH500,
20.63%
on AIME2024, and
20.83%
on AIME2025, surpassing all 8B baselines.
Training Framework:
Trained with
DiRL
, an efficient training framework for diffusion language models.
Strong Baseline:
Built on
SDAR-8B-Chat
, gaining
+11.20%
on MATH500 and
+11.46%
on AIME2024.
Inference
Using LMDeploy
from lmdeploy import pipeline, PytorchEngineConfig, GenerationConfig
from transformers import AutoTokenizer
model_path = "OpenMOSS-Team/DiRL-8B-Instruct"# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Prepare prompts
prompts = [
[{"role": "user", "content": "Solve: If x + 5 = 12, what is x?"}],
]
prompts = tokenizer.apply_chat_template(prompts, tokenize=False, add_generation_prompt=True)
# Configure backend for DLLM inference
backend_config = PytorchEngineConfig(
dtype="float16",
max_prefill_token_num=8192,
cache_max_entry_count=0.8,
dllm_block_length=4,
dllm_denoising_steps=4,
dllm_unmasking_strategy="low_confidence_dynamic",
dllm_confidence_threshold=0.9,
)
# Create inference pipelinewith pipeline(model_path, backend_config=backend_config) as pipe:
gen_config = GenerationConfig(
top_p=1.0,
top_k=50,
temperature=1.0,
do_sample=False, # greedy decoding
max_new_tokens=8192,
)
outputs = pipe(prompts, gen_config=gen_config)
for output in outputs:
print(output.text)
Performance
Model
MATH500
GSM8K
AIME2024
AIME2025
OlympiadBench
Average
Qwen2.5-7B-Instruct
73.78
89.78
8.96
5.63
36.58
42.95
Qwen2.5-32B-Instruct
81.13
94.03
12.92
11.88
45.65
49.12
SDAR-8B-Chat
71.85
89.87
9.17
9.38
36.03
43.26
Trado-8B-Instruct
75.59
91.06
11.67
15.00
40.32
46.73
DiRL-8B-Instruct
83.05
93.03
20.63
20.83
46.40
52.79
Citation
If you use this model in your research, please cite:
@misc{zhu2025dirl,
title={DiRL: An Efficient Training Framework for Diffusion Language Models},
author={Zhu, Ying and Wan, Jiaxin and Liang, Tianyi and Guo, Xu and Liu, Xiaoran and Huang, Zengfeng and He, Ziwei and Qiu, Xipeng},
year={2025},
institution={Fudan University, Shanghai Innovation Institute},
url={https://github.com/OpenMOSS/DiRL}
}
Runs of OpenMOSS-Team DiRL-8B-Instruct on huggingface.co
90
Total runs
1
24-hour runs
-2
3-day runs
-1
7-day runs
15
30-day runs
More Information About DiRL-8B-Instruct huggingface.co Model
DiRL-8B-Instruct huggingface.co is an AI model on huggingface.co that provides DiRL-8B-Instruct's model effect (), which can be used instantly with this OpenMOSS-Team DiRL-8B-Instruct model. huggingface.co supports a free trial of the DiRL-8B-Instruct model, and also provides paid use of the DiRL-8B-Instruct. Support call DiRL-8B-Instruct model through api, including Node.js, Python, http.
DiRL-8B-Instruct huggingface.co is an online trial and call api platform, which integrates DiRL-8B-Instruct's modeling effects, including api services, and provides a free online trial of DiRL-8B-Instruct, you can try DiRL-8B-Instruct online for free by clicking the link below.
OpenMOSS-Team DiRL-8B-Instruct online free url in huggingface.co:
DiRL-8B-Instruct is an open source model from GitHub that offers a free installation service, and any user can find DiRL-8B-Instruct on GitHub to install. At the same time, huggingface.co provides the effect of DiRL-8B-Instruct install, users can directly use DiRL-8B-Instruct installed effect in huggingface.co for debugging and trial. It also supports api for free installation.