Researchers at Nvidia have introduced a novel technique named reinforcement learning pre-training (RLP) that alters the conventional approach to training large language models (LLMs). Traditionally, LLMs undergo a pre-training phase focused on predicting the next token in a text string, learning grammar and associations before advancing to a post-training phase where they develop complex reasoning abilities.
Unlike this sequential method, RLP integrates reinforcement learning into the initial training stage. This approach encourages models to generate internal reasoning—known as a “thought”—prior to predicting the next token, thereby cultivating independent thought at an earlier stage. This shift has reportedly led to substantial improvements in the performance of models in complex reasoning tasks, suggesting potential for more adaptable AI applications.
Nvidia outlines that the typical post-training phase often involves methods such as supervised fine-tuning or reinforcement learning from human feedback (RLHF), relying on curated data sets for effectiveness. The authors of the study argue that the traditional training method does not reflect human cognitive processes accurately, which integrate inputs with prior knowledge in non-linear ways.
RLP aims to enhance this by treating the reasoning process as an action preceding prediction. The model receives a reward based on how much its generated thought improves prediction accuracy compared to a baseline method, effectively allowing it to learn the utility of its reasoning without needing external verification.
In experimental applications with models like Qwen3-1.7B and Nemotron-Nano-12B, RLP demonstrated consistent performance improvements, especially in reasoning-heavy tasks. Early indications suggest that this technique not only amplifies model effectiveness but also mitigates the issue of “catastrophic forgetting,” where models lose previously acquired skills during training.
The technique has shown scalability, achieving notable performance gains while utilizing general web data rather than solely curated datasets. RLP represents a foundational shift in AI model training, potentially paving the way for models that learn to reason more effectively from the outset. Further exploration is needed to fully understand its implications in the broader context of AI training.
Source: https://venturebeat.com/ai/nvidia-researchers-boost-llms-reasoning-skills-by-getting-them-to-think