The remarkable success of OpenAI’s o1 series and DeepSeek-R1 has unequivocally demonstrated the power of large-scale reinforcement learning (RL) in eliciting sophisticated reasoning behaviors and significantly enhancing the capabilities of large language models (LLMs). However, the core training methodologies behind these groundbreaking reasoning models often remain veiled in their technical reports. Recent community efforts have