标题： Klear-Reasoner：通过梯度保留剪切策略优化提升推理能力 显示英文标题

标题： Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving Clipping Policy Optimization

摘要： 我们提出Klear-Reasoner，一种具有长期推理能力的模型，在解决问题时表现出仔细的权衡，实现了在多个基准测试中的卓越性能。 尽管目前社区中已有许多关于推理模型的优秀工作，但由于训练细节披露不全，仍然存在许多难以复现高性能推理模型的问题。 本报告对推理模型进行了深入分析，涵盖了从数据准备、长期思维链监督微调（long CoT SFT）到强化学习（RL）的整个训练后工作流程，并对每个实验组件进行了详细的消融研究。 对于SFT数据，我们的实验表明，少量高质量的数据源比大量多样化的数据源更有效，且困难样本在不进行准确率过滤的情况下也能取得更好的结果。 此外，我们研究了当前剪切机制在RL中的两个关键问题：剪切抑制了关键的探索信号并忽略了次优轨迹。 为了解决这些挑战，我们提出了梯度保留剪切策略优化（GPPO），该方法温和地将梯度从剪切标记反向传播。 GPPO不仅增强了模型的探索能力，还提高了其从负样本中学习的效率。 Klear-Reasoner在数学和编程方面表现出卓越的推理能力，在AIME 2024上得分为90.5%，在AIME 2025上得分为83.2%，在LiveCodeBench V5上得分为66.0%，在LiveCodeBench V6上得分为58.1%。 显示英文摘要

摘要： 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.