The quest for artificial intelligence that can truly reason has long been a central pursuit in the field. The initial stirrings of excitement around OpenAI’s “o1” model ignited a widespread interest in leveraging large-scale reinforcement learning (RL) techniques to build systems capable of sophisticated reasoning. Following this, DeepSeek-R1’s decision to release its model as open-source fueled further enthusiasm and empowered the AI community to vigorously pursue the development of cutting-edge reasoning models.
However, this initial burst of activity was quickly tempered by a significant obstacle. Critical technical details, vitally important for successful replication – specifically, the precise strategies employed for data curation and the intricate recipes governing RL training – were conspicuously absent from DeepSeek-R1’s original report. This omission left researchers in a state of considerable frustration, grappling with the challenge of recreating the reported successes. The consequence was a somewhat fragmented landscape of research, with a multitude of independent efforts exploring different model sizes, various initial checkpoints, and a diverse range of target domains. Despite this intense activity, a comprehensive and consistently effective training recipe remained elusive.
Traditional approaches to training language models for reasoning have primarily concentrated on the domains of mathematics and computer code. These methodologies generally rely on a combination of pre-training on large datasets and supervised fine-tuning to specialize the models for these particular tasks. Early attempts to incorporate reinforcement learning into this process, typically by utilizing domain-specific reward models, yielded only limited gains. This stemmed from the inherent challenges associated with mathematical and coding tasks, where subtle errors can lead to drastically incorrect results.
More recent investigations, spurred by the release of DeepSeek-R1, have explored the use of rule-based verification methods. In the realm of mathematics, these methods often involve requiring specific output formats that enable precise and automated verification of the solution. Similarly, in the context of code, researchers have leveraged the inherent feedback mechanisms of compilation and execution to guide the learning process. However, these approaches have generally been narrowly focused on individual domains, lacking the ability to effectively handle heterogeneous prompts that mix mathematical and coding problems. Furthermore, evaluations have often been restricted to specific benchmarks such as AIME and LiveCodeBench, limiting the generalizability of the findings. Finally, training instability continues to be a persistent issue, often necessitating the use of complex techniques such as progressive response length increases and entropy collapse mitigation.
Now, researchers at NVIDIA are changing the game, as they demonstrate the significant potential of large-scale reinforcement learning to dramatically enhance the reasoning capabilities of relatively small and mid-sized models. Their methods achieve levels of performance that surpass state-of-the-art approaches based on distillation techniques. The NVIDIA approach utilizes a sequential training strategy: first, performing RL training exclusively on math-related prompts, and subsequently switching to prompts focused solely on code.
A Sequential Method for Enhanced Reasoning
The findings? Initial RL training on mathematical problems not only dramatically improves performance on mathematical benchmarks but, surprisingly, also generates a significant boost in code reasoning capabilities. Furthermore, extended iterations of RL training focused specifically on code further augment code performance with only minimal degradation in mathematical performance. This approach highlights a crucial point: mathematical training can act as a strong foundation for more complex reasoning tasks such as coding. This is because mathematical problem-solving often requires a rigorous and systematic approach, emphasizing logical deduction and precise execution. These skills are directly transferable to coding, where attention to detail and the ability to break down complex problems into smaller, manageable steps is crucial for success. Imagine that coding is like building with different sized lego bricks. Math is giving you the ability to organize and measure those lego bricks, so that you can build more steadily.
Integral to the success of the NVIDIA approach is a robust data curation pipeline. This pipeline is meticulously designed to collect challenging prompts characterized by both high difficulty and the availability of high-quality, verifiable answers and test cases. This allows verification-based RL to be applied effectively across both the mathematical and coding domains. Data curation goes beyond simply gathering existing datasets; it involves a deep understanding of the target domains and the types of problems that are most likely to challenge and improve the model. This includes identifying problems with clear, unambiguous solutions and ensuring that the training data is diverse and representative of the range of problems the model will encounter in real-world applications.
Data Curation for Math and Code
The data curation methodology employed by the NVIDIA researchers carefully distinguishes between the requirements for math-only RL and code-only RL. This is a crucial step, as the data characteristics and verification methods differ significantly between these two domains. A one-size-fits-all approach would likely result in suboptimal training, as the model would struggle to generalize across both types of problems.
Math-Only RL: The creation of training data for math-only RL involves merging data from the DeepScaler and NuminaMath datasets. These datasets encompass a wide range of mathematical topics, including algebra, combinatorics, number theory, and geometry. The variety of topics is essential for exposing the model to different mathematical concepts and promoting generalization across different problem types. To maintain the integrity of the data, a rigorous filtering process is applied, utilizing a 9-gram filter to remove redundant or unsuitable content and implementing strict exclusion rules to eliminate potentially problematic entries. The 9-gram filter works by identifying and removing sentences or phrases that are highly similar, preventing the model from simply memorizing specific problem formulations. The DeepSeek-R1 model then plays a crucial role in validating the quality of the questions. Each question is subjected to eight independent attempts by the model, and only those solutions that receive a majority vote of correctness via rule-based verification are retained for inclusion in the final dataset. This process significantly reduces the risk of including incorrect or misleading data, ensuring that the model is trained on high-quality examples.
Code-Only RL: The dataset for code-only RL is constructed using data sourced from modern competitive programming platforms. These platforms provide a rich source of coding problems spanning a diverse array of algorithmic topics. Competitive coding is a great environment to explore, because successful submissions imply that the code solves the problem and meets specific performance requirements which are great rewards. The problems are formatted to align with the function-calling and standard input/output (stdin/stdout) conventions commonly used in these environments. This standardization simplifies the training process and allows the model to interact with the coding problems in a consistent and predictable manner. The researchers undertake a meticulous filtering process to eliminate incompatible problems and meticulously curate comprehensive test cases designed to cover edge cases and boundary conditions. Comprehensive test cases are crucial for ensuring the robustness and reliability of the model. By exposing the model to a wide range of inputs, including those that are likely to cause errors, the researchers can help it learn to handle unexpected situations and produce correct results even in challenging circumstances. Furthermore, each problem is assigned a difficulty score determined through evaluation by the DeepSeek-R1-671B model. This rigorous process results in a high-quality dataset consisting of 8,520 verified coding problems. Difficulty scoring helps in curriculum learning where the model learns simple problems before moving on to more complex ones. This allows the model to progress gradually and avoids overwhelming it with information.
AceReason-Nemotron: Results and Benchmarks
The results of the NVIDIA research are compelling. The AceReason-Nemotron-7B model achieves significant accuracy improvements of 14.5% and 14.6% on the challenging AIME 2024 and 2025 competitions, respectively, when compared to initial SFT models. These results clearly demonstrate the effectiveness of the reinforcement learning approach. By training the model to actively solve problems and receive feedback, the researchers were able to significantly improve its accuracy and performance on challenging mathematical tasks. Furthermore, it demonstrates substantial gains of 14.2% and 8% on the LiveCodeBench v5 and v6 benchmarks, respectively. The LiveCodeBench benchmark evaluates the model’s ability to generate correct and efficient code for a variety of programming problems. The larger 14B variant of the model showcases even greater performance, outperforming larger models such as DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Llama-70B. This achieves best-in-class results among open RL-based reasoning models. The ability to outperform larger models is a testament to the efficiency of the NVIDIA approach. By carefully curating the training data and employing a sequential training strategy, the researchers were able to achieve exceptional performance with a relatively small model.
Compared to state-of-the-art distillation-based models, AceReason-Nemotron-14B outperforms OpenMath-14B/32B by 2.1%/4.4% on AIME benchmarks and OpenCodeReasoning-14B by 1.7%/0.8% on LiveCodeBench. This convincingly demonstrates that RL can achieve higher performance upper-bounds than distillation approaches while maintaining competitive performance against advanced frontier models like QWQ-32B and o3-mini. Distillation approaches involve training a smaller model to mimic the behavior of a larger, more complex model. While this can be an effective way to reduce the size and computational cost of a model, it often comes at the expense of accuracy. The NVIDIA research shows that reinforcement learning can achieve superior performance compared to distillation, suggesting that it is a more promising approach for developing high-performance reasoning models.
The implications of these results are significant. They suggest that large-scale RL has the potential to unlock new levels of reasoning capabilities in AI models, surpassing the limitations of traditional approaches. This has significant implications for a wide range of applications, including scientific discovery, financial modeling, and autonomous driving. The sequential domain-specific training strategy, combined with a robust data curation pipeline, provides a blueprint for future research in this area.
Reinforcement Learning Drives Reasoning Limits
This research underscores the significant potential of reinforcement learning to push the boundaries of model reasoning capabilities. By strategically employing domain-specific training and meticulously curating high-quality data, this allows AI models to solve previously intractable problems and establishes new benchmarks for reasoning model development and ultimately leading to a new generation of AI systems capable of tackling real-world challenges with unprecedented accuracy and efficiency. The ability to reason effectively is a cornerstone of intelligence, and the advances achieved by NVIDIA represent a major step towards realizing the full potential of artificial intelligence. Reasoning allows us to draw conclusions, make predictions, and solve problems in a way that is not possible with simple pattern recognition.
Future research will likely focus on scaling these techniques to even larger models and exploring new data curation strategies to further improve reasoning performance. Scaling to larger models will require significant computational resources and careful attention to training stability. New data curation strategies could involve incorporating more diverse and challenging problems, as well as developing more sophisticated methods for verifying the correctness of solutions. The development of more sophisticated reward functions and exploration strategies will also be crucial for overcoming the challenges associated with training AI models for complex reasoning tasks. Reward functions that encourage exploration of different solution paths and that penalize incorrect or inefficient solutions can help the model learn more effectively. Ultimately, the goal is to create AI systems that can reason, learn, and adapt in a manner similar to humans, enabling them to solve complex problems and make informed decisions across a wide range of domains. Imagine an AI that can adapt across different challenges we present to it, rather than being very good at one specific skill.
Moreover, the use of RL offers advantages beyond raw accuracy. RL agents can learn to optimize for a variety of objectives, such as efficiency, robustness, and interpretability. This is in contrast to traditional supervised learning approaches, which typically focus solely on maximizing accuracy. For example, an RL agent could be trained to generate code that is not only correct but also efficient and easy to understand. This capability is particularly important in safety-critical applications, where it is essential to ensure that AI systems are reliable and predictable.
The work by NVIDIA highlights the growing importance of data curation in AI research. The quality of the training data has a significant impact on the performance of AI models, and carefully curated datasets are essential for achieving state-of-the-art results. Data curation is not just about collecting large amounts of data; it is about selecting and preparing data that is relevant, accurate, and representative of the problem being solved. The data curation pipeline developed by NVIDIA is a valuable resource for researchers working on reasoning models, and it could be adapted for use in other domains as well.
The combination of large-scale RL, domain-specific training, and robust data curation has proven to be a winning formula for improving the reasoning capabilities of AI models. As these techniques continue to evolve, we can expect to see even more impressive advances in the field of AI, and we hope to see continued advancements of AI models in the near future. The future of AI is bright, and the research being conducted by NVIDIA is helping to pave the way for a new generation of intelligent machines. We are going to see exciting changes in this space.