The quest to build truly intelligent large language models (LLMs) often boils down to enhancing their reasoning capabilities. While advancements in size and architecture have been monumental, injecting genuine reasoning skills remains a frontier. Traditionally, reinforcement learning (RL) is applied after pretraining to fine-tune models. However, what if we could instill reasoning much earlier, right from the foundational pretraining stage?
NVIDIA AI researchers are addressing this very challenge with a groundbreaking new approach: Reinforcement Learning Pretraining (RLP). This innovative framework promises to build more robust and capable LLMs by making reasoning an intrinsic part of their learning journey, rather than an afterthought.
Unpacking Reinforcement Learning Pretraining (RLP)
NVIDIA AI has introduced Reinforcement Learning Pretraining (RLP), a training objective that injects reinforcement learning into the pretraining stage rather than deferring it to post-training. The core idea is simple and testable: treat a short chain-of-thought (CoT) as an action sampled before next-token prediction and reward it by the information gain it provides on the observed next token, measured against a no-think EMA baseline. This produces a verifier-free, dense, position-wise reward that can be applied to ordinary text streams at pretraining scale.
At its heart, RLP uses a single neural network with shared parameters. This network has a dual role: first, it samples a Chain-of-Thought (CoT) policy, essentially simulating a moment of “thinking” by the model. Second, it then scores the next token in the sequence, conditioned on that thought process.
To measure the value of this “thinking,” RLP employs a cleverly designed mechanism. A slowly updated Exponential Moving Average (EMA) teacher model provides a “no-think” counterfactual – essentially, what the model would predict without a CoT. The reward for the thought is then calculated based on the information gain: the difference in log-likelihood between the prediction with thought and the prediction without it. This maximizes the expected information gain, pushing the model to generate thoughts that genuinely improve its understanding and prediction.
Why RLP Marks a Technical Leap Forward
The significance of RLP extends beyond merely shifting reinforcement learning earlier in the pipeline. It addresses fundamental limitations found in previous attempts at “reinforcement pretraining.” Many earlier variants relied on sparse, binary correctness signals or required external proxy filters.
Why this matters technically: unlike prior “reinforcement pretraining” variants that rely on sparse, binary correctness signals or proxy filters, RLP’s dense, verifier-free reward attaches position-wise credit wherever thinking improves prediction, enabling updates at every token position in general web-scale corpora without external verifiers or curated answer keys.
This “verifier-free, dense, position-wise signal” is a game-changer. It means RLP can operate on vast, general web-scale corpora without needing human-annotated answer keys or complex external verifiers. The model learns to reward itself for effective internal reasoning, making it incredibly scalable and adaptable to diverse datasets.
Furthermore, Reinforcement Learning Pretraining (RLP) is designed to be orthogonal to traditional post-training pipelines like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) or RL with AI Feedback (RLVR). This means that RLP’s foundational reasoning improvements can compound with subsequent alignment techniques, leading to even more advanced and reliable LLMs.
Real-World Impact: Impressive Results and Efficiency
The theoretical elegance of RLP is powerfully validated by its empirical results across different LLM architectures and benchmarks. The improvements are not just incremental; they demonstrate a substantial leap in reasoning capabilities.
On the Qwen3-1.7B-Base model, pretraining with RLP improved the overall math+science average by ~19% versus the base model and ~17% versus compute-matched continuous pretraining (CPT). Crucially, even after identical post-training (SFT + RLVR), the RLP-initialized model retained a ~7–8% relative advantage, with the largest gains observed on reasoning-heavy benchmarks such as AIME25 and MMLU-Pro. This highlights the durable nature of RLP’s embedded reasoning.
Another compelling demonstration came from applying RLP to a Nemotron-Nano-12B v2, a hybrid Mamba-Transformer checkpoint. The overall average increased dramatically from 42.81% to 61.32%. Even more impressively, it achieved an absolute +23% gain on scientific reasoning. These gains were achieved even though the RLP run used approximately 200 billion fewer tokens for training compared to the baseline, underscoring RLP’s remarkable data efficiency and architecture-agnostic behavior.
In direct comparisons with other “reinforcement pretraining” variants, like RPT, RLP consistently outperformed on math, science, and overall averages under matched data and compute conditions. This superior performance is largely attributed to RLP’s continuous information-gain reward mechanism, a significant advantage over RPT’s sparse binary signals and entropy-filtered tokens.
These findings strongly suggest that the improvements from RLP are a direct result of its innovative objective design, rather than merely throwing more computational budget at the problem. It fundamentally changes how models learn to reason.
Conclusion
NVIDIA’s Reinforcement Learning Pretraining (RLP) represents a significant advancement in the field of large language model development. By reframing pretraining to directly reward “think-before-predict” behavior using a verifier-free, information-gain signal, RLP yields durable reasoning gains that persist through identical SFT+RLVR and extend across diverse architectures.
This method’s objective—contrasting CoT-conditioned likelihood against a no-think EMA baseline—integrates cleanly into large-scale AI training pipelines without the need for curated verifiers. RLP is not just another post-training add-on; it’s a practical and powerful upgrade to next-token pretraining, promising to make future LLMs inherently more capable of complex reasoning.
As LLMs continue to evolve, approaches like RLP will be crucial in unlocking their full potential. To dive deeper into the technical details and explore the code, check out the Paper, Code, and Project Page from NVIDIA. This is an exciting step forward in building truly intelligent AI.
