NVIDIA Researchers Propose Reinforcement Learning Pretraining (RLP): Reinforcement as a Pretraining Objective for Building Reasoning During Pretraining

Understanding the Target Audience for NVIDIA’s Reinforcement Learning Pretraining (RLP)

The target audience for NVIDIA’s Reinforcement Learning Pretraining (RLP) includes AI researchers, machine learning engineers, data scientists, and business leaders in technology-focused industries. These individuals are typically engaged in developing AI models or implementing AI solutions in their organizations. Their primary pain points include:

• Difficulty in achieving efficient model training that balances performance and resource consumption.
• Challenges in improving the reasoning capabilities of AI models, particularly in complex domains like mathematics and science.
• Need for scalable and robust AI solutions that can adapt to various data sources without heavy reliance on curated datasets.

Their goals revolve around:

• Enhancing the accuracy and reasoning abilities of AI models.
• Reducing training time and resource expenditure while maximizing model performance.
• Staying updated on the latest advancements in AI methodologies to maintain a competitive edge.

Interests include:

• Innovative AI training techniques, especially those that leverage reinforcement learning.
• Research publications and case studies demonstrating practical applications of AI advancements.
• Networking with peers and experts in the field to exchange knowledge and strategies.

Communication preferences typically lean towards:

• Technical reports and research papers that provide in-depth analyses and empirical data.
• Webinars and conferences that offer opportunities for real-time interaction and discussion.
• Online forums and communities for collaborative problem-solving and idea sharing.

Overview of NVIDIA’s Reinforcement Learning Pretraining (RLP)

NVIDIA AI has introduced Reinforcement Learning Pretraining (RLP), a training objective that incorporates reinforcement learning during the pretraining stage. The core concept involves treating a short chain-of-thought (CoT) as an action sampled before next-token prediction, rewarding it based on the information gain it provides regarding the observed next token. This is measured against a no-think Exponential Moving Average (EMA) baseline, producing a verifier-free, dense, position-wise reward applicable to ordinary text streams at pretraining scale.

Mechanism: Information-Gain Rewards with an EMA Counterfactual

RLP utilizes a single network with shared parameters to:

• Sample a CoT policy π_θ(c_t|x₎ and score the next token p_θ(x_t|x_{, ct)}.
• Employ a slowly updated EMA teacher p_ϕ(x_t|x₎ to provide a no-think counterfactual.

The per-token reward is calculated as the log-likelihood ratio:

r(c_t) = log p_θ(x_t|x_{, ct) — log pϕ(xt|x)}

Training updates focus exclusively on thought tokens using a clipped surrogate with per-token importance ratios and group-relative advantages. This design maximizes expected information gain, connecting expected rewards to reductions in cross-entropy.

Significance of RLP

Unlike prior reinforcement pretraining methods that rely on sparse, binary correctness signals, RLP’s dense, verifier-free reward system assigns position-wise credit wherever reasoning enhances prediction. This enables updates at every token position across general web-scale corpora without external verifiers or curated answer keys.

Understanding the Results

In experiments with the Qwen3-1.7B-Base model, pretraining with RLP resulted in a ~19% improvement in overall math and science performance compared to the base model, and ~17% compared to compute-matched continuous pretraining (CPT). Following identical post-training (SFT + RLVR) across all variants, the RLP-initialized model maintained a ~7–8% relative advantage, particularly on reasoning-heavy benchmarks.

For the Nemotron-Nano-12B v2 model, applying RLP yielded an overall average increase from 42.81% to 61.32%, with a notable +23% gain in scientific reasoning, despite using ~200B fewer tokens.

Positioning vs. Post-Training RL and Data Curation

RLP is distinct from post-training pipelines, demonstrating compounding improvements after standard alignment. The reward is computed from model log-evidence, allowing it to scale across domain-agnostic corpora while avoiding the limitations of curated datasets. In compute-matched comparisons, RLP consistently outperformed alternatives, indicating that improvements stem from the objective design rather than training budget.

Key Takeaways

• RLP establishes reasoning as a pretraining objective, rewarding information gain over a no-think EMA baseline.
• It offers a verifier-free, dense, position-wise signal, facilitating scalable pretraining updates on every token.
• Results from Qwen3-1.7B show a +19% improvement vs the base model and +17% vs compute-matched CPT; with identical SFT + RLVR, RLP retains ~7–8% gains.
• For Nemotron-Nano-12B v2, the overall average rose from 42.81% to 61.32%, achieving +23 points on scientific reasoning with ~200B fewer tokens used.

Conclusion

RLP reframes pretraining to directly reward «think-before-predict» behavior using a verifier-free, information-gain signal. This approach yields durable reasoning gains that persist through identical SFT + RLVR and extend across various architectures. The method’s design integrates seamlessly into large-scale pipelines, representing a practical upgrade to next-token pretraining.

For further details, check out the Paper, Code, and Project Page.