【Introduction】Recently, the NVIDIA research team introduced the next-generation tool-using language model Nemotron-Research-Tool-N1, enabling AI not only to call tools but also to perform deep reasoning. How exactly is this achieved? Today, let's take a look at this groundbreaking research.
The NVIDIA research team has released a tool-using language model called Nemotron-Research-Tool-N1 (Tool-N1 for short), which allows AI to call external tools more intelligently. The most striking aspect is that this small model, with only 7B and 14B parameters, actually beat GPT-4o in mainstream benchmark tests!
This is somewhat like equipping AI with a "toolbox" and teaching it how to first think and then act, similar to humans. This breakthrough in capability holds significant meaning for our expectations of AI achieving more complex functions.
1. Limitations of the Old Paradigm: Why weren't previous tool-using models good enough?
Let's first consider a question: When you need to use a new tool, how do you learn? Most people first understand the tool's purpose, then think about how to use it, and finally perform the actual operation.
However, the current mainstream AI training methods for tool usage lack the crucial "thinking" step. They mainly adopt the supervised fine-tuning (SFT) method, which only teaches the model to "imitate" how others call tools, without understanding why. This leads to two main problems:
(1) Lack of reasoning ability: Some models completely ignore the reasoning process, focusing only on whether the final tool call is correct.
(2) Pseudo-reasoning: Although some models generate text that appears to be thinking, they are actually just imitating the superficial patterns in the training data and do not truly understand.
This is like teaching a child to memorize the multiplication table without teaching them the meaning of multiplication. When faced with new situations, this superficial learning will be inadequate.
2. Nemotron-Research-Tool-N1: AI's "Understanding-Based Learning"
Inspired by the DeepSeek R1 model, the NVIDIA team adopted a brand new training paradigm – rule-based reinforced learning. The biggest feature of this method is:
Instead of directly teaching the AI what to do, it lets the AI figure out the best approach on its own.
Specifically, the training process for the Tool-N1 model is as follows:
(1) Structured thinking template: The model is required to perform explicit reasoning within tags before calling tools.
(2) Binary reward mechanism: The model receives a reward only when the reasoning format is correct and the tool call is accurate.
(3) Flexible evaluation criteria: It does not require strict character matching but focuses on the functional correctness of the tool call.
The core value of this training method lies in enabling the model to learn reasoning itself, rather than simply memorizing or imitating. This is like not just teaching a child to recite the multiplication table but helping them understand the essence of multiplication, enabling them to solve various multiplication problems.
3. Results: How did a small model beat GPT-4o?
Data is the most convincing. In the BFCL and API-Bank mainstream tool-using benchmark tests, the Tool-N1 model showed impressive performance:
In the BFCL test:
(1) Tool-N1-7B (based on Qwen2.5-7B-Instruct): Surpassed GPT-4o
(2) Tool-N1-14B (based on Qwen2.5-14B-Instruct): Led comprehensively, setting a new SOTA record.
In the API-Bank test:
(1) Tool-N1-7B had a 4.12% higher accuracy rate than GPT-4o.
(2) Tool-N1-14B had a 5.03% higher accuracy rate than GPT-4o.
This is an important signal: The method combining reinforced learning and explicit reasoning is more effective than purely supervised learning. More importantly, even under the same data conditions, the Tool-N1 training method significantly outperforms traditional SFT methods.
4. In-depth Analysis: Why is this method so effective?
The research team conducted a series of in-depth experiments, revealing several key findings:
(1) Binary reward is better than fine-grained reward: A simple correct/incorrect reward mechanism is more effective than complex partial rewards because it prevents the model from pursuing partial rewards and neglecting overall correctness.
(2) Mandatory thinking format is crucial: When the reasoning format requirement is removed, model performance significantly drops (from 80.38% to 76.24%), indicating that structured thinking is vital for tool usage capability.
(3) Scale effect is significant: This training method shows better results with increased model scale, performing best particularly at the 7B and 14B scales.
(4) Base model selection is important: At the same scale, models based on Qwen2.5 perform significantly better than the LLaMA series, possibly because Qwen itself has stronger reasoning capabilities.
5. Conclusion
The success of Tool-N1 points to a new direction for the development of AI's tool usage capabilities. The advantages of this method are:
(1) Less annotation needed: No manual annotation of the reasoning process is required, reducing data preparation costs.
(2) Stronger generalization ability: By learning reasoning rather than imitation, the model can better handle new situations.
(3) Higher efficiency: Compared to large models with equivalent performance, small and medium-sized models are more efficient.
This technology may be applied in various scenarios in the future, such as: intelligent assistants, programming assistance, information retrieval systems, etc. Imagine your AI assistant not only helping you search for information but also calling calculators, calendars, email, and other tools, while simultaneously understanding your real needs and making reasonable decisions.
In the future, AI will not just be an information porter, but will become an assistant capable of independent thinking and flexible tool utilization.
NVIDIA's Nemotron-Research-Tool-N1 represents a new milestone in AI's tool usage capability. It cultivates the model's intrinsic reasoning ability through reinforced learning, rather than merely superficial tool call imitation. This method not only achieved breakthroughs in performance but, more importantly, provides a training paradigm closer to human learning.
For us, this research reminds us: In the field of AI, sometimes a better learning method is more important than more data and larger models.
Recommended Reading
Counter-intuitive discovery in AI training: Adding "toxic" data can actually make language models better?
Survey of Multimodal Reasoning Large Models: The Evolution from Perception to Reasoning, Thinking, and Planning
X-REASONER: Breaking Dimensional Walls, Moving Towards Cross-Modal and Cross-Domain Generalization Reasoning