### Introduction: The Need for Broad Reasoning in LLMs

Large Language Models (LLMs) are rapidly evolving, and their ability to "reason" is becoming increasingly important. We're moving beyond simple text generation, and now expect these models to solve problems, draw inferences, and even understand complex concepts. One of the key techniques used to improve reasoning in LLMs is Reinforcement Learning (RL), which essentially trains the model through rewards and penalties.

However, there's a catch: much of the current open-source work in RL for LLMs is heavily focused on specific areas like math and coding. Think of it like this: imagine training a chef only on desserts; they might excel at pastry, but lack the skills for savory dishes. This narrow focus limits our understanding of how well RL *actually* improves reasoning in LLMs across the board. It raises questions about whether the gains we see in math and code translate to other areas, or if we're simply creating highly specialized models with limited general knowledge.

One of the biggest roadblocks to broader application of RL is the lack of reliable "reward signals" and high-quality training data in diverse fields. It's relatively easy to automatically check if a math problem is solved correctly or if code compiles. But how do you reward a model for, say, making a logical inference or understanding a scientific concept? This is where the research paper comes in, introducing **GURU**, a carefully designed RL corpus spanning six different reasoning domains:

*   **Math:** Numerical and symbolic problem-solving.
*   **Code:** Program synthesis and debugging.
*   **Science:** Understanding and applying scientific principles.
*   **Logic:** Deductive and inductive reasoning.
*   **Simulation:** Reasoning within simulated environments.
*   **Tabular:** Analyzing and drawing conclusions from structured data.

By creating GURU, the researchers aim to address the limitations of current RL-based reasoning approaches. The paper revisits existing RL methods using this new, more comprehensive dataset, highlighting that insights from one domain don't always hold true in others. This suggests that a one-size-fits-all approach to RL for reasoning may not be effective and that domain-specific strategies might be necessary.


## GURU: A Multi-Domain Dataset for Reliable RL

The quest for more intelligent and reliable AI agents hinges on the availability of high-quality training data. The GURU dataset tackles this challenge head-on by providing a meticulously curated reinforcement learning (RL) corpus designed to test and train agents across a diverse range of reasoning tasks. Think of it as a comprehensive "workout" routine for your AI, pushing it to master different cognitive skills.

GURU spans six distinct reasoning domains:

*   **Math:** Tests numerical reasoning and problem-solving abilities. Example: solving algebraic equations or calculus problems.
*   **Code:** Focuses on code generation, understanding, and debugging. Example: writing a function to sort a list or identifying errors in existing code.
*   **Science:** Assesses understanding of scientific principles and the ability to apply them. Example: predicting the outcome of a chemical reaction or explaining a physics concept.
*   **Logic:** Challenges agents with logical deduction and inference tasks. Example: solving logic puzzles or determining the validity of arguments.
*   **Simulation:** Involves reasoning within simulated environments, requiring agents to make decisions based on simulated physics or game rules. Example: navigating a virtual maze or playing a strategy game.
*   **Tabular:** Centers on reasoning with structured data presented in tables. Example: analyzing sales data to identify trends or making predictions based on financial reports.

What truly sets GURU apart is its rigorous data curation pipeline, ensuring that the data is not only diverse but also reliable and of high quality. This pipeline consists of several key stages:

1.  **Data Sourcing and Synthesis:** The process begins by gathering data from existing datasets and synthesizing new tasks to cover a wide range of difficulty levels and reasoning skills.

    Here are some of the data sources used in creating the GURU dataset, categorized by domain:

    *   **Math:** OR1, DAPO, DeepScaler
    *   **Code:** LeetCode, TACO-Verified, PrimeIntellect, LiveCodeBench, WebInstruct-Verified, Code I/O (PyEdu)
    *   **Science:** WebInstruct-Verified
    *   **Logic:** ARC-AGI, Zebra Puzzle, Ordering Puzzles, Graph Search
    *   **Simulation:** BARC
    *   **Tabular:** HiTab, MultiHierTT

2.  **Domain-Specific Reward Function Design:** A critical step is defining how agents are "rewarded" for their actions in each domain. GURU uses three types of reward functions:

    *   **Rule-based:** Rewards are based on predefined rules.
    *   **Execution-based:** Rewards are based on the successful execution of a program or action.
    *   **Model-based verification:** Rewards are based on the predictions of a model.

    Think of it as setting up a clear and consistent scoring system for each type of "game" the AI is playing.

3.  **Deduplication:** This stage eliminates redundant data entries to prevent the model from memorizing specific examples, ensuring the AI learns generalized problem-solving skills rather than rote memorization.

4.  **Filtering (Heuristic and Model-Based):** The final stage involves filtering out noisy or trivial examples using both heuristic rules and machine learning models. This ensures that the dataset contains challenging and informative examples that promote effective learning.

The meticulous nature of this data pipeline is what makes GURU a valuable resource for the RL community. By carefully sourcing, cleaning, and filtering the data, the creators have ensured that the dataset is both diverse and reliable, enabling researchers to develop more robust and intelligent AI agents. The final corpus consists of 92k examples, each paired with a verifiable reward signal. This level of detail and verification makes GURU a benchmark dataset for training and evaluating RL agents.


## The GURU Data Curation Pipeline

The GURU data curation pipeline refines raw data through a series of steps to produce a high-quality dataset for reinforcement learning. This diagram illustrates the sequential flow of data through the five stages of the pipeline, from initial data sourcing to the final GURU-92K dataset.

```mermaid
flowchart LR
    A[Data Sourcing] --> B[Data Deduplication]
    B --> C[Heuristic Filtering]
    C --> D[Reward Design]
    D --> E[Difficulty Filtering]
    E --> F[GURU-92K Dataset]
    style A fill:#f9f,stroke:#333,stroke-width:2px
    style B fill:#ccf,stroke:#333,stroke-width:2px
    style C fill:#f9f,stroke:#333,stroke-width:2px
    style D fill:#ccf,stroke:#333,stroke-width:2px
    style E fill:#f9f,stroke:#333,stroke-width:2px
    style F fill:#ccf,stroke:#333,stroke-width:2px
```

This diagram provides a clear overview of the data processing steps involved in creating the GURU dataset. By visualizing the flow, we can easily understand the sequential dependencies and the transformations applied at each stage, which ultimately leads to a refined dataset suitable for training.


### Cross-Domain RL: Unveiling Domain-Specific Behaviors

The research paper dives into the fascinating world of cross-domain reinforcement learning (RL), exploring how knowledge gained in one type of problem can boost performance in others. Think of it like this: if you've mastered driving a car, you'll likely pick up driving a truck faster than someone who's never been behind the wheel. But how does this "transfer" work when we're dealing with AI agents tackling diverse reasoning tasks?

To find out, the researchers designed an experiment centered around training reasoning models on different datasets. They used a dataset called GURU-18K which contains a mix of problems spanning various domains like Math, Code, Logic, Science, Simulation, and Tabular reasoning. They trained models on individual domain subsets (e.g., only Math problems) and on a mixed dataset containing all domains. This allowed them to analyze how single-domain training compared to cross-domain learning.

A key concept that emerges is "differential transferability" – the idea that some domains benefit more from cross-domain learning than others. The study found that Math, Code, and Science domains saw a bigger boost from cross-domain RL. Why? The authors suggest pretraining exposure is the key. If a model has already been exposed to similar concepts during its initial training, it can more easily adapt to new, related tasks in a different domain. It's like having a solid foundation in algebra making it easier to grasp calculus.

However, Logic, Simulation, and Tabular reasoning were different. These domains seemed to require more in-domain training, meaning they performed better when trained specifically on problems from their own domain. This suggests that these domains might have unique characteristics or problem-solving approaches that don't easily transfer from other areas. Perhaps the specific rules and constraints in Logic problems or the unique dynamics of a Simulation environment require specialized learning.

The difficulty of the task also plays a role. Easier problems within Math and Code domains showed positive transfer from other domains, while more challenging benchmarks didn't improve as much. This makes intuitive sense: foundational skills are more readily transferable than highly specialized expertise.

Interestingly, the researchers found that training on a uniformly mixed dataset of reasoning tasks was surprisingly effective. In many cases, these mixed-domain models matched or even exceeded the performance of models trained solely on a single domain. This highlights the potential of cross-domain RL to create more general and adaptable AI agents.

Finally, the paper unveils interesting insights into how RL training affects the way models generate responses. In single-domain training, the researchers observed that models trained on Code, Logic, and Tabular tasks tended to "contract" their outputs, becoming more concise. On the other hand, models trained on Science and Math tasks became more "verbose," generating longer responses. Understanding these reward and response length dynamics is crucial for building more robust and reliable RL systems. It suggests that different domains might incentivize different communication strategies, a factor that needs careful consideration when designing RL agents for complex reasoning tasks.


## GURU Models: Achieving State-of-the-Art Reasoning

The heart of the research lies in the training and evaluation of the GURU models. Two versions were created: GURU-7B (7 billion parameters) and GURU-32B (32 billion parameters). Both were trained on the complete GURU dataset, and the results speak for themselves. These models aren't just another incremental improvement; they represent a significant leap in reasoning performance.

GURU-7B achieved a score of 43.29%, while GURU-32B reached 54.24%. To put that in perspective, GURU-7B outperforms the best baseline models by a whopping 9.0%, and GURU-32B exceeds them by 6.7%. This level of improvement is substantial in the rapidly evolving landscape of large language models.

But what makes these results so compelling? It's not just about achieving a high score on a single benchmark. The GURU models demonstrated superior and well-balanced performance across *all* six domains evaluated. This is crucial because many existing "state-of-the-art" models often excel in specific areas but falter in others. Think of it like a star athlete who can only play one sport well. A truly versatile AI needs to be proficient across a wide range of reasoning tasks.

The models were put through a rigorous testing suite, including:

*   **General Reasoning:** AIME24, MATH500, ARC-AGI, Zebra Puzzle, GPQA, SuperGPQA
*   **Code Generation:** HumanEval, MBPP, LiveCodeBench, CodeI/O
*   **Evaluation:** CruxEval-I, CruxEval-O, FinQA, HiTab, MultiHiertt, IFEval, and LiveBench

This comprehensive evaluation ensures that the GURU models are not just memorizing solutions but are genuinely capable of reasoning across diverse problem types. A key metric used in several of these benchmarks is "Pass@k", which estimates the probability of finding at least one correct solution within the first k generated samples. The GURU models showcased strong Pass@k performance, suggesting a higher likelihood of producing correct and relevant solutions more efficiently.

One key observation around baselines is that while some domain-specific models can achieve impressive results within their niche, they often lack the generalizability of the GURU models. For example, a model fine-tuned solely for mathematical problem-solving might outperform GURU-7B on MATH500, but it would likely struggle with code generation or logical reasoning puzzles. The strength of the GURU models lies in their ability to handle a broad spectrum of reasoning tasks effectively.


### Conclusion: Towards General-Purpose Reasoning with RL

This paper tackles the ambitious goal of achieving general-purpose reasoning in AI through reinforcement learning (RL). The authors introduce GURU, a carefully designed RL dataset aimed at pushing the boundaries of reasoning in large language models.

A key finding of their work is that the effectiveness of RL in enhancing reasoning abilities is highly dependent on the specific domain. Think of it like this: training a model to play chess might make it a grandmaster on the board, but it won't necessarily make it better at solving your taxes! This highlights the need for multi-domain RL – exposing models to a wide variety of reasoning tasks – to truly achieve general-purpose intelligence.

To that end, the authors developed GURU-7B and GURU-32B, two language models trained with RL on the GURU dataset, and achieved state-of-the-art results among open models. Importantly, the authors are releasing the GURU dataset, their models, an evaluation suite, and the training code. By open-sourcing these resources, they are inviting the broader research community to join in exploring the potential of RL for reasoning.

Looking ahead, this research opens up several exciting avenues:

*   **Multi-Agent Reasoning:** Could LLMs trained via RL operate as effective agents in complex, collaborative environments?
*   **Preference Alignment:** How can we best align language model behavior with human preferences? Methods like direct preference optimization and group relative policy optimization are promising approaches.
*   **LLMs as Teachers:** Can LLMs guide traditional RL agents by acting as "teachers" providing curated learning experiences and initial task assignments?

The field also faces ongoing challenges, such as overcoming pattern-matching limitations and ensuring that models can genuinely reason in multimodal contexts. It remains uncertain whether assessments using puzzle-based evaluations accurately reflect real-world reasoning demands. The GURU dataset and models represent a valuable contribution to the ongoing effort to unlock the full potential of reinforcement learning for reasoning in AI.


GURU: A multi-domain RL dataset for LLM reasoning. Shows RL's domain-dependent effects and achieves state-of-the-art open model performance, advancing general reasoning.

RecursivAI

GURU: Revisiting RL for LLM Reasoning Across Domains