Improving the reasoning capabilities of large language models (LLMs) has recently attracted significant attention in the AI community. The current paradigm for developing strong reasoning models typically involves a two-stage approach: supervised fine-tuning (SFT) with distilled data, followed by reinforcement learning (RL). While the open-source community has flourished with increasingly available open-source datasets, many critical training details remain unclear.

In this study, we present a comprehensive and open-source pipeline for training a high-performance reasoning model, named Klear-Qwen3-Thinking, starting from the Qwen3-8B-Base. We balance training stability and exploratory behavior in RL through multiple strategies. Klear-Qwen3-Thinking-Preview achieves 76.4% on AIME 2025 and 63.9% on LiveCodeBench V5, improving +13.7% and +8.8% over its SFT baseline, respectively. Notably, Klear-Qwen3-Thinking-Preview yields better performance than Qwen3-8B (Thinking mode), and competitive performance as DeepSeek-R1-0528-Qwen3-8B in math and coding, without distilling from DeepSeek-R1-0528.

👨‍💻 Github, 🤗 HF Model, 🤗 HF Dataset, 📖 [Tech Report](comming soon), 🔎 Evaluation results

Performance in comparison with SOTA models on AIME 24&25 and LiveCodeBench v5. Klear-SFT and Klear-Preview refer to Klear-Qwen3-Thinking-SFT and Klear-Qwen3-Thinking-Preview, respectively. Among 7B and 8B models, we outperform AceReason-Nemotron-1.1-7B (AceReason) and Qwen3-8B. Although we do not use the DeepSeek-R1-0528 dataset, we achieve comparable results to DeepSeek-R1-0528-Qwen3-8B. Additionally, compared to larger models like Qwen3-32B and DeepSeek-R1 (0120), we also demonstrate significant advantages.