Limitations of Enhanced Learning in the Narrow Reasoning Area
Reinforcement learning RL shows strong potential to enhance the reasoning capabilities of LLM, especially in leading systems such as OpenAI-O3 and DeepSeek-R1. However, most RL studies focus on mathematics and code, thus limiting their general applicability. This narrow range raises two questions: Our understanding of how RL improves reasoning may not generalize beyond these areas, and the resulting models often lack versatility. Due to the lack of reliable reward signals and curated datasets, extending RL to a wider inference task is challenging, which are easier to define in mathematical and code-based terms but more difficult in open inference domains.
Narrow field priorities and generalized challenges
Reinforcement learning of RL has become a popular way to improve the reasoning skills of LLM, especially after the success of using models like GPT-3 and DeepSeek-R1 of OpenAI. Following many open source work, focusing mainly on the math and coding domains. Although these models perform well in their wall ni, their reasoning is not always generalized to a broader task. At the same time, the research explores how RL affects reasoning. Some studies have shown that RL does not teach new skills, but can improve the model’s ability to access existing inference patterns. However, newer work suggests that extended RL training may unlock entirely new inference strategies.
Introduction to Master Dataset: Multi-Domain RL Benchmark Testing
Researchers from UC San Diego, MBZUAI, Carnegie Mellon and Purdue introduced Guru, a 92 K-example RL dataset that covers six areas of reasoning: mathematics, code, science, science, logic, simulation, and tables. Each domain is tailored to reward features and strict filtering. The master’s training model shows that RL results depend to a large extent on the familiarity of the domain: common domains benefit from cross-domain RL, while unfamiliar domains require intradomain training for significant improvement. Their models GURU-7B and GURU-32B outperformed the previous open model by up to 7.9% in 17 tasks. These findings highlight the value of RL’s domain-specific effects and the broad multidomain reasoning benchmarks.
Cross-domain and internal domain enhancement learning effect
To better understand how RL supports cross-domain reasoning, researchers trained models for personal and hybrid domain data from the Master Dataset. They found that fields such as mathematics, code and science benefited more from cross-domain RL, which may be due to their powerful presence in pre-training. The mixed domain training performed is also performed or better than single domain training, suggesting that combining various tasks can enhance general reasoning. However, training on harder examples can improve performance in the field, but reduce accuracy for other functions in other ranges. These findings suggest that data diversity and balance difficulty are key to effective, transferable reasoning skills.
Master model architecture and evaluation strategy
The study trained models of 7B and 32 B sizes using the GURU dataset to explore how combining multiple domains during RL can improve inference capabilities. The model was evaluated using the VERL framework and the GRPO algorithm using consistent metrics to evaluate a variety of tasks, including mathematics, code, logic, science, simulation and tables. The results show that the Master model performs better than domain-specific benchmarks and performs well on invisible tasks. It is worth noting that analysis of Pass@K shows that performance depends on task type, model size, and decoding settings. Larger models benefit more from RL, and adjusting sampling parameters such as temperature and TOP-P can help improve model diversity and inference coverage.
Abstract: Grandmaster’s General Reasoning
In short, Guru is a curated RL dataset containing 92,000 high-quality, verifiable examples spanning six inference domains: mathematics, code, science, logic, simulation, and tables. Unlike previous RL studies that focused primarily on mathematics and code, Masters achieve a broader inference research by providing domain-specific reward signals. The researchers trained two models GURU-7B and GURU-32B, which achieved state-of-the-art results on 17 benchmark tasks, especially outstanding over the range of the expected period. Their findings show that RL can both perfect existing knowledge and cultivate new reasoning abilities. Publicly publish all data, models and code to support further general reasoning research.
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Sana Hassan, a consulting intern at Marktechpost and a dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. He is very interested in solving practical problems, and he brings a new perspective to the intersection of AI and real-life solutions.
