NVIDIA RLP: Reinforcement Learning Pretraining for AI Models

Introduction

NVIDIA has introduced RLP (Reinforcement Learning Pretraining), a groundbreaking approach that fundamentally changes how large language models learn to reason. Instead of treating reasoning as an afterthought added during post-training, RLP integrates reinforcement learning directly into the pretraining stage, teaching models to "think before they predict" from the very beginning.

Published on September 30, 2025, by NVIDIA's Advanced Deep Learning Research (ADLR) team, RLP represents a paradigm shift in AI model development. The method rewards models for generating useful chains-of-thought (CoT) that actually improve next-token prediction, creating a verifier-free, dense, and scalable approach to teaching reasoning at the foundation level.

Understanding RLP: How It Works

The Core Concept

RLP treats chain-of-thought generation as an explicit action taken before predicting each next token. Rather than simply predicting the next word in a sequence, the model first generates an internal thought process, then uses that reasoning to make better predictions.

The RLP Process:

Step 1: Sample internal thought - The model generates a chain-of-thought about what might come next
Step 2: Predict with context - The model predicts the observed token using both the original context and the CoT
Step 3: Calculate reward - The model receives a reward based on how much the CoT improved prediction accuracy
Step 4: Learn from feedback - The model learns to generate more useful thoughts through reinforcement learning

Verifier-Free Information Gain Reward

Unlike traditional methods that require external verifiers or labeled data, RLP uses a verifier-free information gain reward system:

Dense signal - Rewards are assigned at every position where thinking improves prediction
Self-supervised - No external verifiers or human annotations needed
Scalable - Works on any text corpus, from academic papers to web content
Dynamic baseline - Uses an EMA (Exponential Moving Average) baseline for stable training

The reward is calculated as the increase in log-likelihood of the observed token when the chain-of-thought is present compared to a "no-think" baseline. This creates a natural, self-supervised signal that teaches the model when and how to reason effectively.

Performance Results: Qwen3-1.7B-Base

Experimental Setup

NVIDIA tested RLP on Qwen3-1.7B-Base, comparing three models through identical post-training:

BASE - Original base model
CPT - Compute-matched Continuous Pre-training baseline
RLP - Model trained with Reinforcement Learning Pretraining

All three models underwent the same post-training pipeline with Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verified Rewards (RLVR) to ensure fair comparison.

Pretraining Phase Results

During pretraining alone, RLP demonstrated superior performance:

+19% improvement over the original base model on average benchmarks
+17% improvement over compute-matched CPT baseline
Strong generalization across math, science, and reasoning tasks
Consistent gains before any post-training applied

Post-Training Performance

The benefits of RLP compound rather than disappear after post-training:

+8% relative advantage maintained after full post-training
+3 absolute points on science benchmarks over CPT after alignment
Durable reasoning foundations that persist through SFT and RLVR
Broad generalization beyond math to multiple domains

Key Takeaway

RLP establishes a decisive pretraining advantage that compounds with traditional post-training methods, proving that foundational reasoning capabilities built during pretraining create lasting improvements.

Scaling to Larger Models: Nemotron-Nano-12B-V2

Impressive Efficiency Gains

NVIDIA applied RLP to an intermediate checkpoint of Nemotron-Nano-12B-V2 (trained on 19.8 trillion tokens) for just 250 million additional tokens:

Overall average increased from 42.81% to 61.32%
+35% relative improvement on average across all benchmarks
200 billion fewer tokens used compared to the base model
Cross-architecture generalization demonstrated on different model family

Domain-Specific Improvements

RLP achieved particularly strong results across multiple domains:

Science Reasoning:

+23% absolute improvement - Most striking gain across all domains
Enhanced multi-step reasoning capabilities
Better handling of complex scientific concepts

Math Performance:

Moderate improvements in mathematical reasoning
Consistent gains across different math benchmarks
Improved problem-solving approaches

General Reasoning:

Broad improvements across diverse reasoning tasks
Better logical inference capabilities
Enhanced context understanding

Scaling Insights

The Nemotron results demonstrate that:

RLP benefits amplify at scale - Larger models see even stronger improvements
Architecture agnostic - Works across different model families (Qwen, Nemotron)
Token efficient - Achieves better results with significantly fewer training tokens
Production ready - Practical for real-world deployment scenarios

Generalization Across Diverse Corpora

Testing on Six Dataset Types

NVIDIA tested RLP on Qwen3-1.7B-Base across six different corpus families:

Academic papers - Scientific and research publications
Textbooks - Educational materials across subjects
Web crawl - Diverse internet content
SFT-style data - Supervised fine-tuning datasets
Mixed corpora - Combined dataset types
General-purpose - Broad domain coverage

Consistent Performance Gains

RLP demonstrated remarkable consistency:

7-9% average improvements across all corpus types
Strongest gains on SFT-style and general-purpose data
True cross-domain transfer - Simultaneous improvements across all benchmarks
No domain-specific tuning required

Finding Reasoning Everywhere

One of RLP's most impressive characteristics is its ability to find reasoning signals in unexpected places:

Web crawl data - Even non-curated internet content provides reasoning opportunities
No curation needed - Eliminates costly dataset preparation
Data efficiency - Leverages existing pretraining corpora
Universal applicability - Works with the same data streams as standard pretraining

This proves that RLP can enhance reasoning ability using ordinary pretraining data, making it truly scalable without requiring expensive, specialized datasets.

Key Advantages of RLP

Scalability

Works at pretraining scale - Operates on massive text streams
No special datasets required - Uses standard pretraining corpora
Architecture agnostic - Generalizes across different model families
Size scalable - Benefits increase with larger models

Efficiency

Token efficient - Achieves better results with fewer tokens
Compute effective - Integrates seamlessly into existing pretraining
Time efficient - Single unified training phase instead of multi-stage pipelines
Cost effective - Reduces need for expensive post-training data curation

Performance

Strong baseline improvements - Significant gains before post-training
Compounding benefits - Advantages persist and strengthen through alignment
Broad generalization - Improvements across math, science, reasoning, and more
Robust gains - Consistent performance across diverse benchmarks

Practical Benefits

Verifier-free - No external verification systems needed
Dense rewards - Learning signal at every position
Self-supervised - No human annotations required
Production ready - Practical for real-world deployment

Technical Implementation

Reward Mechanism

RLP calculates rewards by contrasting predictions:

With CoT - Model prediction conditioned on chain-of-thought
Without CoT - Baseline prediction using EMA model without thinking
Information gain - Reward equals improvement in next-token prediction
Position-wise credit - Assigns credit wherever thinking helps

Dynamic EMA Baseline

The Exponential Moving Average baseline provides:

Stable training - Smooths out reward variance
Meaningful comparison - Compares current model to its recent past
Adaptive learning - Baseline evolves with model capabilities
Credit assignment - Helps identify truly useful reasoning

Integration with Pretraining

RLP augments standard next-token prediction:

Seamless integration - Works alongside maximum likelihood training
Unified objective - Single training phase combines prediction and reasoning
Scalable infrastructure - Uses existing pretraining pipelines
Minimal overhead - Efficient implementation for large-scale training

Implications for AI Development

Paradigm Shift in Model Training

RLP challenges the traditional approach to AI model development:

Traditional Approach:

Pretrain on next-token prediction
Fine-tune on supervised data
Add reasoning through post-training RL

RLP Approach:

Pretrain with integrated reasoning from day one
Build foundational reasoning capabilities
Compound benefits through post-training

Future of AI Reasoning

RLP suggests several important directions for AI development:

Reasoning as foundation - Treating reasoning as core capability, not add-on
Unified training - Single-phase training that combines prediction and reasoning
Scalable methods - Approaches that work with ordinary pretraining data
Efficient learning - Better results with fewer tokens and less curation

Practical Applications

Models trained with RLP could excel at:

Complex problem-solving - Enhanced multi-step reasoning capabilities
Scientific reasoning - Improved understanding of scientific concepts
Mathematical tasks - Better mathematical problem-solving
General reasoning - Stronger logical inference across domains
Autonomous agents - More reliable reasoning for agentic AI systems

Research Contributions

Novel Methodology

RLP introduces several innovative concepts:

Reinforcement as pretraining - Novel method to integrate RL directly into pretraining at scale
Verifier-free rewards - Dense, self-supervised signal without external verification
Thinking as action - Treats CoT generation as exploratory action in RL framework
Information gain objectives - Uses prediction improvement as natural reward signal

Comprehensive Evaluation

The research includes extensive validation:

Multiple model sizes - From 1.7B to 12B parameters
Multiple architectures - Qwen and Nemotron families
Diverse datasets - Six different corpus types tested
Ablation studies - Systematic analysis of key components
Post-training analysis - Shows benefits persist through alignment

Open Research

NVIDIA has made the research accessible:

Published paper - Detailed methodology and results
Code release - Implementation available on GitHub
Reproducible results - Clear experimental setup and benchmarks
Community contribution - Advancing the field of AI reasoning

Conclusion

NVIDIA's RLP (Reinforcement Learning Pretraining) represents a fundamental rethinking of how we build reasoning capabilities into AI models. By integrating reinforcement learning directly into pretraining, RLP teaches models to think before they predict, creating foundational reasoning abilities that persist and compound through subsequent training stages.

Key Achievements:

+19% improvement over base models and +17% over continuous pretraining on Qwen3-1.7B
+35% average gain on Nemotron-Nano-12B-V2 using 200B fewer tokens
+23% absolute improvement in science reasoning on larger models
Consistent generalization across diverse corpora and model architectures
Compounding benefits that persist and strengthen through post-training

RLP's verifier-free, dense, and scalable approach makes it practical for real-world deployment while achieving state-of-the-art results. By finding reasoning signals in ordinary pretraining data, RLP eliminates the need for costly dataset curation and establishes a new paradigm where reasoning is a core capability built from the foundation up.

This research opens exciting possibilities for building AI models that naturally integrate reasoning into their prediction processes, potentially leading to more capable, reliable, and efficient AI systems across all domains.

Ready to dive deeper into AI concepts? Explore our AI Fundamentals course to understand the building blocks of modern AI, check out our glossary for key terms like chain-of-thought and reinforcement learning, or visit our models catalog to learn about the latest AI models.

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