Core Insights - The article discusses significant breakthroughs in AI large models, particularly in reasoning capabilities across mathematics, logic puzzles, and code generation, highlighting the potential of Reinforcement Learning with Verified Reinforcement (RLVR) technology [1][3]. Group 1: Research Findings - The OpenDataLab team constructed a multi-domain evaluation framework encompassing three categories: Math, Code, and Puzzle, with customized reward strategies for different training data [3][7]. - Experiments using the Qwen2.5-7B series model achieved an overall average performance of 56.57, significantly outperforming any dual-domain combinations [3][24]. - Key findings include the inter-support between Puzzle and Math data, the cross-domain mixing effects of Code reasoning, and the importance of reward design tailored to task difficulty [6][12][26]. Group 2: Performance Metrics - In single-domain training, the Base model showed a 75 percentage point accuracy improvement on the CountDown task, while enhancing its ability to solve logic puzzles [10]. - The Instruct model demonstrated superior performance in programming tasks, maintaining or improving performance across most out-of-domain tasks [12]. - The accuracy of the Instruct model reached 99.14 on the KK dataset, with significant improvements in the Zebra task [15]. Group 3: Training Strategies - The research emphasizes the necessity of template consistency during training and evaluation, as mismatched templates can lead to drastic performance drops [21][24]. - Curriculum learning strategies, including the "Policy Refresh" approach, were shown to enhance model performance by gradually increasing task difficulty [23][29]. - Reward design was found to be critical, with different strategies yielding varying results based on task complexity and data sparsity [26]. Group 4: Future Directions - The team calls for the expansion of data categories into new fields such as Science and General Reasoning, and the exploration of model adaptability with Llama and DeepSeek [28].