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Kwai-Klear/
Klear-Reasoner-8B

Name: Klear-Reasoner-8B
Availability: InStock

safetensors

qwen3

Kwai-Klear/KlearReasoner-MathSub-30K

Kwai-Klear/KlearReasoner-CodeSub-15K

2508.07629

Kwai-Klear/Klear-Reasoner-8B-SFT

finetune

apache-2.0

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✨ Klear-Reasoner-8B

We present Klear-Reasoner, a model with long reasoning capabilities that demonstrates careful deliberation during problem solving, achieving outstanding performance across multiple benchmarks. We investigate two key issues with current clipping mechanisms in RL: Clipping suppresses critical exploration signals and ignores suboptimal trajectories. To address these challenges, we propose Gradient-Preserving clipping Policy Optimization (GPPO) that gently backpropagates gradients from clipped tokens.

Resource	Link
📝 Preprints	Paper
🤗 Daily Paper	Paper
🤗 Model Hub	Klear-Reasoner-8B
🤗 Dataset Hub	Math RL
🤗 Dataset Hub	Code RL
🐛 Issues & Discussions	GitHub Issues
📧 Contact	suzhenpeng13@163.com

📌 Overview

_{Benchmark accuracy of Klear-Reasoner-8B on AIME 2024/2025 (avg@64), LiveCodeBench V5 (2024/08/01-2025/02/01, avg@8), and v6 (2025/02/01-2025/05/01, avg@8).}

Klear-Reasoner is an 8-billion-parameter reasoning model that achieves SOTA performance on challenging math and coding benchmarks:

Benchmark	AIME 2024	AIME 2025	LiveCodeBench V5	LiveCodeBench V6
Score	90.5 %	83.2 %	66.0 %	58.1 %

The model combines:

Quality-centric long CoT SFT – distilled from DeepSeek-R1-0528.
Gradient-Preserving Clipping Policy Optimization (GPPO) – a novel RL method that keeps gradients from clipped tokens to boost exploration & convergence.

Evaluation

When we expand the inference budget to 64K and adopt the YaRN method with a scaling factor of 2.5. Evaluation is coming soon, stay tuned.

📊 Benchmark Results (Pass@1)

Model	AIME2024 avg@64	AIME2025 avg@64	HMMT2025 avg@64	LCB V5 avg@8	LCB V6 avg@8
AReal-boba-RL-7B	61.9	48.3	29.4	34.3	31.0†
MiMo-7B-RL	68.2	55.4	35.7	57.8	49.3
Skywork-OR1-7B	70.2	54.6	35.7	47.6	42.7
AceReason-Nemotron-1.1-7B	72.6	64.8	42.9	57.2	52.1
POLARIS-4B-Preview	81.2	79.4	58.7	58.5†	53.0†
Qwen3-8B	76.0	67.3	44.7†	57.5	48.4†
Deepseek-R1-0528-Distill-8B	86.0	76.3	61.5	61.0†	51.6†
OpenReasoning-Nemotron-7B	84.7	78.2	63.5	65.6†	56.3†
Klear-Reasoner-8B-SFT	75.6	70.1	57.6	58.5	49.6
Klear-Reasoner-8B	83.2	75.6	60.3	61.6	53.1
w/ 64K Inference Budget	90.5	83.2	70.8	66.0	58.1

We report the average pass@1 results (avg@n), with all other evaluation metrics following the DeepSeek-R1 assessment framework (temperature=0.6, top_p=0.95).

🧪 Training

Configure the experimental environment

bash

git clone https://github.com/Kwai-Klear990901/Klear_Reasoner
cd Klear_Reasoner
pip install -r requirements.txt

For the code, we use Firejail for the sandbox environment. Additionally, we implemented multi-process control based on Pebble, enabling automatic resource reclamation upon task timeout. For mathematics, we use math_verify for judging.

Using Ray for Multi-Node Training

For multi-node training, ensure all nodes are started and connected via Ray before executing the training script. Below is a brief setup guide for Ray across multiple machines:

Step 1: Start Ray on the Head Node (node0)

On the first node (typically called node0), run:

ray start --head --dashboard-host=0.0.0.0

Get the IP address of the master node.

MASTER_IP=$(hostname -I | awk '{print $1}')

Step 2: Connect Other Nodes (e.g., node1)

On each additional worker node (e.g., node1), run the following, replacing the IP with that of your head node:

ray start --address=\"$MASTER_IP:6379\"

RL Training

Run the following script on the master node to start the training task.

bash

bash recipe/dapo/perf_run_dapo_ours_math.sh # For Math RL
bash recipe/dapo/perf_run_dapo_ours_code.sh # For Code RL

In the startup script, you need to set the following variables:

bash

YOUR_MODEL_PATH="<your_model_path>"
CKPTS_SAVE_DIR="<ckpts_save_path>"
YOUR_TRAIN_FILE="<train_data_path>"
YOUR_TEST_FILE="<test_data_path>"

Evaluation

When we expand the inference budget to 64K and adopt the YaRN method with a scaling factor of 2.5.

The evaluation data for AIME24, AIME25, and HMMT2025 are available in our GitHub repository under the benchmarks directory. For LiveCodeBench, please download the data from the official website.

You can run the following commands to perform inference and evaluation:

bash

git clone https://github.com/Kwai-Klear990901/KlearReasoner  
cd KlearReasoner/benchmarks  
python inference.py --model <KlearReasoner-8B_path> --n 64 --dataset_path ./benchmarks/aime24.qs.jsonl  
python judge_math.py <path_to_inference_results>

🤝 Citation

If you find this work helpful, please cite our paper:

bibtex

@misc{su2025cegppocontrollingentropygradientpreserving,
      title={CE-GPPO: Controlling Entropy via Gradient-Preserving Clipping Policy Optimization in Reinforcement Learning}, 
      author={Zhenpeng Su and Leiyu Pan and Minxuan Lv and Yuntao Li and Wenping Hu and Fuzheng Zhang and Kun Gai and Guorui Zhou},
      year={2025},
      eprint={2509.20712},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2509.20712}, 
}

bibtex

@article{DBLP:journals/corr/abs-2508-07629,
  author       = {Zhenpeng Su and
                  Leiyu Pan and
                  Xue Bai and
                  Dening Liu and
                  Guanting Dong and
                  Jiaming Huang and
                  Wenping Hu and
                  Fuzheng Zhang and
                  Kun Gai and
                  Guorui Zhou},
  title        = {Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving
                  Clipping Policy Optimization},
  journal      = {CoRR},
  volume       = {abs/2508.07629},
  year         = {2025},
  url          = {https://doi.org/10.48550/arXiv.2508.07629},
  doi          = {10.48550/ARXIV.2508.07629},
  eprinttype    = {arXiv},
  eprint       = {2508.07629},
  timestamp    = {Sat, 13 Sep 2025 14:46:27 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2508-07629.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}