NVIDIA Turbocharges AI Math and Code Reasoning with AceReason-Nemotron Reinforcement Learning Model
–
Reasoning capacity stands at the core of modern AI architectures. The launch of OpenAI’s o1 model sparked intense interest in leveraging large-scale reinforcement learning to build systems capable of logical deduction and complex problem solving. Open sourcing of DeepSeek-R1 energized community efforts but left out key guidelines for gathering training material and applying specific RL procedures. Missing methods for sampling tasks as well as detailed reward designs created a gap that made it difficult for other teams to replicate the reported progress. Those gaps prompted a range of trial setups in different environments, employing varied model sizes, initial checkpoints, distilled reasoning objectives, and task domains that yielded mixed outcomes.
Many groups experimented with pretraining and supervised fine-tuning before adding RL to math or code challenges. Early attempts relied on reward models tuned to each subject area and produced modest benefit. Teams began testing rule-based checks for each kind of problem. Arithmetic tests required outputs in fixed formats so that verification scripts could confirm correctness automatically. In code challenges, the approach depended on compiling and running snippets to verify functional accuracy. Those tests covered only one type of prompt at a time. Benchmarking was limited to AIME or LiveCodeBench and training runs often faced stability problems. Researchers adopted methods like staged expansion of response length and entropy collapse avoidance to reduce crashes.
A research team at NVIDIA reported that large-scale RL can push strong small and medium-size models past the limits of distillation-based methods. Their strategy involved two sequential RL passes. The first pass trained on math-only questions. The second pass focused on code-only samples. Using straightforward reward formulas for each stage, the math sequence led to higher scores on mathematical assessments and improved code reasoning performance to some degree. Further cycles of code-only training lifted results on programming tasks and produced only slight drops on math benchmarks after extended code focus. Early experiments showed that models in the 7B to 14B parameter range often failed to match larger models when relying solely on distillation. This procedure offers a clear path for boosting ability across categories and exceeding distillation-only outcomes for models of varied sizes.
Data collection played a key role in making verification-based RL effective across both domains. For math-only training, the pipeline combined DeepScaler and NuminaMath question sets covering algebra, combinatorics, number theory and geometry. A 9-gram filter removed near duplicates of text fragments. Strict exclusion rules filtered out problems with vague or malformed statements. A DeepSeek-R1 checkpoint then attempted each question eight times. Only those problems with at least five identical solutions passed to a rule-based validator. That validator checked output formatting and answer correctness before adding each item to the final math training set. The result was a high-fidelity math corpus free of ambiguous items and ready for reward-based tuning.
In the code-only phase, prompts came from modern competitive programming sources. Each problem needed to accept either function calls or standard input and produce valid output that passed multiple test cases. Items that failed these requirements were dropped. Researchers crafted extra test cases to capture boundary and corner conditions. They applied the DeepSeek-R1-671B model to run sample solutions and assigned a difficulty rating based on the success rate. That process produced 8,520 verified coding problems, each linked to a comprehensive set of test scripts. These curated challenges supplied clear reward signals throughout the code-focused reinforcement sessions.
Benchmark comparisons tracked progress before RL, after math-only training, and following code-only updates. On the AIME 2024 exam, the AceReason-Nemotron-7B model improved accuracy by 14.5% over its supervised fine-tuned baseline. On AIME 2025, it gained 14.6%. In LiveCodeBench version 5, performance rose by 14.2% and in version 6 by 8%. The 14B variant outscored larger open models such as DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Llama-70B. When compared to top distillation-based systems, AceReason-Nemotron-14B beat OpenMath-14B and 32B by 2.1% and 4.4% on AIME. It surpassed OpenCodeReasoning-14B by 1.7% and 0.8% on LiveCodeBench. These figures represent aggregate improvements over the initial supervised fine-tuned baselines and rival the outcomes reported by closed-source commercial systems across both mathematical and coding evaluations.
Those results demonstrate that reinforcement learning can raise the performance ceiling of reasoning models beyond supervised distillation techniques. The 14B agent maintained competitive results against frontier models such as QWQ-32B and o3-mini. The sequential emphasis on math then code shows that domain-specific coaching steps can coexist without severe conflict or major performance regressions. Training stability remained reliable using basic safeguards, reducing the need for complex entropy regularization methods. This pattern differs from earlier pipelines that required fully separate regimes for each category or experienced severe trade-offs when trying to cover multiple domains at once.
The study underscores the importance of combining systematic data curation with targeted reinforcement passes. The final pipeline guarantees that every sample carries a high-confidence answer and an associated suite of checks that confirm correctness across both algebraic and algorithmic cases. By leveraging that structured feedback, the team produced models that solved a greater share of benchmark tasks than any open RL-based approach before. The authors note that such strategies could translate to domains beyond mathematics and programming, including symbolic reasoning or decision-making under uncertainty. These methods provide a clear blueprint for teams seeking to adapt RL setups to new problem collections while preserving accuracy and reliability.
