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PretrainZero Framework Taps Human-Like Active Learning to Boost AI Reasoning

In a significant stride toward artificial general intelligence, researchers have unveiled PretrainZero, a novel framework that integrates reinforcement learning (RL) directly into the foundational pretraining phase of large language models (LLMs). This technique bypasses a major bottleneck in AI development—the reliance on costly, verifiable rewards—by teaching models to actively seek out and master challenging information in noisy, real-world data like Wikipedia.

Traditionally, applying sophisticated RL methods to enhance reasoning (known as Reinforcement Learning with Verifiable Rewards, or RLVR) has been confined to post-training stages using highly specialized, verified datasets (like those found in mathematics or coding). This reliance creates a “data-wall,” preventing the expansive reasoning benefits of RL from generalizing across broad domains.

PretrainZero breaks this barrier by employing a mechanism inspired by human active learning: students don’t learn most efficiently by randomly reviewing facts, but by focusing on the material they find most informative and yet-to-be-mastered.

The core of PretrainZero is a coupled, adversarial learning system built upon self-supervised objectives, requiring no external reward models or supervised fine-tuning. The system comprises two policies operating simultaneously:

  1. The Mask Generator (The Challenger): This policy actively selects contiguous word spans within a text passage to mask. Crucially, it is rewarded for selecting spans that are challenging but predictable—areas representing genuine knowledge gaps, not just random noise.
  2. The Mask Predictor (The Learner): This policy attempts to recover the masked span by generating a Chain-of-Thought (CoT) reasoning process. It receives a verifiable binary reward (an exact match to the ground truth).

Imagine the model reading a historical text. Instead of passively masking a common phrase like “the capital of [mask],” the mask generator actively seeks a phrase that requires deeper contextual understanding, such as masking “cinematography” in a sentence describing the invention of the motion picture. By optimizing against this challenging internal adversary, the predictor policy is forced to develop robust, generalizable reasoning capabilities.

This method drastically improves learning efficiency, especially when training on low-information-density corpuses. Previous RL attempts using simple techniques like random or entropy-based masking (which selects tokens with high uncertainty) often failed or led to training collapse when confronted with the noise and inconsistencies of raw Wikipedia data.

The results are substantial. When applied to base models such as Qwen3-4B-Base, PretrainZero demonstrated robust reasoning gains, improving average accuracy by 8.43 points on MMLU-Pro, 5.96 points on SuperGPQA, and 10.60 points on math benchmarks during the pretraining stage. These benefits persist and generalize when the models are later applied to downstream RLVR tasks.

By proving that RL can be trained effectively on general-domain data like Wikipedia, PretrainZero establishes a powerful new foundation for creating more generalized and robust reasoning LLMs, potentially unlocking future models capable of solving complex problems across virtually any domain.