Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving
  Clipping Policy Optimization
We present Klear-Reasoner, a model with long reasoning capabilities that
demonstrates careful deliberation during problem solving, achieving outstanding
performance across multiple benchmarks. Although there are already many
excellent works related to inference models in the current community, there are
still many problems with reproducing high-performance inference models due to
incomplete disclosure of training details. This report provides an in-depth
analysis of the reasoning model, covering the entire post-training workflow
from data preparation and long Chain-of-Thought supervised fine-tuning (long
CoT SFT) to reinforcement learning (RL), along with detailed ablation studies
for each experimental component. For SFT data, our experiments show that a
small number of high-quality data sources are more effective than a large
number of diverse data sources, and that difficult samples can achieve better
results without accuracy filtering. In addition, 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. GPPO not only
enhances the model's exploration capacity but also improves its efficiency in
learning from negative samples. Klear-Reasoner exhibits exceptional reasoning
abilities in mathematics and programming, scoring 90.5\% on AIME 2024, 83.2\%
on AIME 2025, 66.0\% on LiveCodeBench V5 and 58.1\% on LiveCodeBench V6.
689ab4aefab6fdd2e52ac483
Zhenpeng Su
claimed_verified
2025-08-14T13:39:37.925Z
61c2cf8d1172fa7969904d99
https://cdn-avatars.huggingface.co/v1/production/uploads/61c2cf8d1172fa7969904d99/R10G5h3d9Q_YQ__Hc-H4k.jpeg
suu
user
Suu
689ab4aefab6fdd2e52ac484
Leiyu Pan
689ab4aefab6fdd2e52ac485
Xue Bai
689ab4aefab6fdd2e52ac486
Dening Liu
689ab4aefab6fdd2e52ac487
Guanting Dong
claimed_verified
2025-08-13T07:20:12.675Z
61cd4b833dd34ba1985e0753
https://cdn-avatars.huggingface.co/v1/production/uploads/61cd4b833dd34ba1985e0753/BfHfrwotoMESpXZOHiIe4.png
KABI
user
dongguanting
689ab4aefab6fdd2e52ac488
Jiaming Huang
689ab4aefab6fdd2e52ac489
Wenping Hu
689ab4aefab6fdd2e52ac48a
Guorui Zhou
Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving
  Clipping Policy Optimization
We present Klear-Reasoner, a model with long reasoning capabilities that
demonstrates careful deliberation during problem solving, achieving outstanding
performance across multiple benchmarks. Although there are already many
excellent works related to inference models in the current community, there are
still many problems with reproducing high-performance inference models due to
incomplete disclosure of training details. This report provides an in-depth
analysis of the reasoning model, covering the entire post-training workflow
from data preparation and long Chain-of-Thought supervised fine-tuning (long
CoT SFT) to reinforcement learning (RL), along with detailed ablation studies
for each experimental component. For SFT data, our experiments show that a
small number of high-quality data sources are more effective than a large
number of diverse data sources, and that difficult samples can achieve better
results without accuracy filtering. In addition, 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. GPPO not only
enhances the model's exploration capacity but also improves its efficiency in
learning from negative samples. Klear-Reasoner exhibits exceptional reasoning
abilities in mathematics and programming, scoring 90.5\% on AIME 2024, 83.2\%
on AIME 2025, 66.0\% on LiveCodeBench V5 and 58.1\% on LiveCodeBench V6.
Klear-Reasoner, a model with long reasoning capabilities, achieves high performance across benchmarks through detailed post-training workflows, including long Chain-of-Thought supervised fine-tuning and reinforcement learning with Gradient-Preserving clipping Policy Optimization.
long reasoning Chain-of-Thought supervised fine-tuning long CoT SFT reinforcement learning RL Gradient-Preserving clipping Policy Optimization GPPO exploration signals suboptimal trajectories backpropagation gradients clipped tokens AIME LiveCodeBench