TLDR: RLP (Reinforcement Learning Pre-training) is a novel method that introduces reinforcement learning into the pre-training phase of large language models. It encourages models to “think” (generate chain-of-thought) before predicting the next token, rewarding them based on the information gain these thoughts provide. This verifier-free, dense reward signal improves reasoning capabilities, scales across different model sizes and architectures, and enhances performance even after post-training, outperforming traditional pre-training and other reinforcement pre-training methods on diverse datasets.
In the rapidly evolving field of artificial intelligence, the way large language models (LLMs) are trained is constantly being refined. Traditionally, these powerful models begin their learning journey by predicting the next token in a sequence, a process known as next-token prediction. While effective, this method doesn’t explicitly encourage complex reasoning or the integration of world knowledge early in the training phase. Reasoning abilities are typically introduced much later, during post-training phases like supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
A new research paper introduces an innovative approach called RLP, which stands for Reinforcement Learning Pre-training. This method aims to integrate the core spirit of reinforcement learning—exploration—into the pre-training phase itself. The central idea behind RLP is to treat “chain-of-thought” as an exploratory action. Imagine a model “thinking” before it predicts the next word. RLP rewards the model based on how much information gain these internal thoughts provide for accurately predicting subsequent tokens.
Essentially, RLP encourages models to develop independent thinking behavior much earlier in their training. The reward signal is calculated by measuring the increase in the log-likelihood of the next token when the model conditions on both the context and a sampled reasoning chain, compared to conditioning on the context alone. This ingenious approach provides a “verifier-free” and dense reward signal, meaning it doesn’t require external human feedback or task-specific checkers. This allows for efficient training across vast amounts of ordinary text data during pre-training.
RLP reframes reinforcement learning for reasoning as a pre-training objective on standard text, effectively bridging the gap between simple next-token prediction and the emergence of useful chain-of-thought reasoning. The researchers demonstrated the effectiveness of RLP through comprehensive experiments. For instance, pre-training with RLP on the Qwen3-1.7B-Base model significantly lifted the overall average across an eight-benchmark math-and-science suite by 19%. Even more impressively, when applied to the hybrid Nemotron-Nano-12B-v2 model, RLP increased the overall average from 42.81% to 61.32%, and raised the average on scientific reasoning by 23%. These results highlight RLP’s scalability across different architectures and model sizes.
The benefits of RLP are not just confined to pre-training; they also compound with post-training. Models pre-trained with RLP showed even greater improvements after undergoing identical supervised fine-tuning and reinforcement learning with verifier rewards (RLVR) post-training regimens. This indicates that RLP establishes robust reasoning foundations that are not overwritten but rather enhanced by subsequent alignment processes.
Furthermore, RLP was shown to outperform other methods like Reinforcement Pre-training (RPT) and continuous pre-training (CPT) even when matched for data and compute. Unlike RPT, which uses sparse binary rewards and often relies on filtering “easy” tokens, RLP provides a continuous improvement signal at every position, training on full documents. This dense, per-token information-gain reward offers richer credit assignment, leading to better performance.
A key advantage of RLP is its generalizability. It successfully extracts a powerful reasoning signal from diverse, general-purpose web corpora, academic papers, and math textbooks, unlike methods that require narrow, curated datasets. This versatility means RLP can be applied to any data format, making it a broadly applicable reinforcement pre-training objective.
The research paper, available at arXiv:2510.01265, details the methodology, experimental setup, and comprehensive results. It introduces RLP as a verifier-free information-gain objective that augments next-token prediction by rewarding thoughts in proportion to their predictive utility. The authors also developed a practical and stable training algorithm and provided theoretical guarantees linking expected reward to reductions in cross-entropy.
Also Read:
- RoRecomp: Making LLMs Reason More Concisely and Efficiently
- DIVER: A New Approach to Enhance LLM Reasoning Through Diverse Exploration
In conclusion, RLP represents a significant step forward in LLM training. By integrating reinforcement learning into the pre-training phase, it fosters explicit reasoning abilities earlier, leading to models that are not only more accurate but also possess stronger and more durable reasoning capabilities across diverse domains and architectures.
