Reinforcement learning has shown strong potential to improve reasoning in large language models like OpenAI-O3 and DeepSeek-R1. Most experiments to date have focused on mathematical proofs and code generation, which narrows the scope of RL advances. This specialization raises two concerns: it remains unclear whether RL-driven gains extend to other types of reasoning, and models trained in this way may struggle with tasks outside their niche. Extending RL into open-ended reasoning is further complicated by the challenge of defining robust reward signals and assembling curated datasets for domains whose success criteria are less concrete than those in math or programming.
After breakthroughs with GPT-3 and DeepSeek-R1, reinforcement learning became a go-to approach for refining LLM reasoning. An array of open-source initiatives adopted RL methods primarily in the areas of mathematics and software code. These models often deliver impressive results in their target domains but struggle when asked to tackle puzzles, scientific questions, or logical challenges. Studies debate whether RL mainly helps models retrieve pretrained reasoning patterns or if extended RL training sparks entirely new problem-solving strategies.
To address these issues, researchers at UC San Diego, MBZUAI, Carnegie Mellon University, and Purdue University created GURU, a reinforcement learning dataset of 92,000 examples spanning six reasoning domains: math, code, science, logic, simulation, and tabular data. The team crafted domain-specific reward functions and applied strict quality filters to keep examples clear, challenging, and verifiable.
In experiments with GURU, familiar areas such as math, coding, and basic science see the greatest gains when models train on mixed-domain samples, suggesting cross-domain RL can reinforce reasoning links already present in pretrained weights. Less familiar areas like logical inference or tabular problem solving require targeted, in-domain RL training for significant improvement. Training on the full mix of domains matches or exceeds single-domain runs, highlighting how diversity of tasks builds flexible reasoning skills.
The researchers also tested the effect of concentrating RL on high-difficulty examples. Although this approach increases accuracy on challenging tasks within the selected domain, it comes at the expense of lower performance on simpler tasks from other domains. This pattern points to the necessity of a balanced training curriculum: overemphasis on the hardest cases can narrow a model’s ability to apply basic reasoning elsewhere.
To quantify these effects, the team trained both 7 billion- and 32 billion-parameter models using the Verl framework and the GRPO algorithm. They evaluated performance across 17 benchmark tasks covering math, code, science, logic, simulation, and tables—using uniform metrics such as Pass@k. Results show that GURU-trained models outperform domain-specific baselines by as much as 7.9 percent and generalize better to unseen prompts. Model scale matters: the larger 32 billion variant gains more from RL. Adjusting sampling parameters like temperature and top-p further enhances output diversity and reasoning depth.
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