混合数学编程逻辑数据，一次性提升AI多领域强化学习能力

Core Insights - The article discusses significant advancements in AI large models, particularly in reasoning capabilities across various domains such as mathematics, programming, and logic puzzles, highlighting the potential of Reinforcement Learning with Verified Results (RLVR) technology [1][3]. Group 1: Multi-Domain Evaluation Framework - The research team developed a multi-domain evaluation framework encompassing Math, Code, and Puzzle data, with customized reward strategies for different training datasets [3][14]. - The experiments utilized the Qwen2.5-7B series model, achieving an overall average performance of 56.57 after joint training across the three domains, outperforming any dual-domain combinations [3][31]. Group 2: Key Findings from Experiments - The interaction between Puzzle and Math data significantly enhances overall model performance, indicating a synergistic effect [6]. - Instruct models demonstrate better generalization of coding abilities to other domains compared to Base models, showcasing the cross-domain mixing effect [7]. - Diverse data can improve model robustness, but complex designs are necessary to address potential conflicts among Math, Code, and Puzzle domains [8]. Group 3: Training Methodologies and Strategies - Incorporating Supervised Fine-Tuning (SFT) before reinforcement learning can significantly enhance model performance [9]. - Template consistency is crucial; mismatched training and evaluation templates can lead to substantial performance drops, indicating challenges in generalization robustness [10][29]. - Regularly updating reference models and optimizer states during curriculum learning can improve model stability and performance [11]. Group 4: Performance in Specific Domains - In single-domain training, models show significant performance improvements on specific tasks, but cross-domain effects can be complex, leading to both synergistic and detrimental interactions [19]. - The Base model's accuracy improved by approximately 75 percentage points on the CountDown task after targeted training, but optimizing for Math may negatively impact Code tasks [20]. - In the Code domain, SFT enhances programming task performance, with Instruct models showing higher performance ceilings compared to Base models [21]. Group 5: Cross-Domain Interactions - The Math + Puzzle combination improved Math task performance to 49.72, demonstrating effective cross-domain knowledge transfer, while Code tasks benefited from the addition of Puzzle or Math data [25]. - The combination of all three domains resulted in superior overall performance and robustness, avoiding performance collapse in specific tasks [31]. Group 6: Curriculum Learning and Reward Design - Curriculum learning has proven effective in SFT, and its application in RLVR is still being explored, with a focus on difficulty gradients and the "Policy Refresh" strategy [33]. - Reward design is critical; different strategies yield varying results based on task complexity and data sparsity, influencing the success of RLVR [35][37]. Group 7: Future Directions - The research team calls for the expansion of data categories into new fields such as Science and General Reasoning, and the exploration of model adaptability for Llama and DeepSeek [39].