Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining
  Levels
Authors: name: Zhepeng Cen, name: Haolin Chen, name: Shiyu Wang, name: Zuxin Liu, name: Zhiwei Liu, name: Ding Zhao, name: Silvio Savarese, name: Caiming Xiong, name: Huan Wang, name: Weiran Yao
Organization: Salesforce AI Research

Large Language Models (LLMs) have achieved remarkable success through
imitation learning on vast text corpora, but this paradigm creates a
training-generation gap and limits robust reasoning. Reinforcement learning
(RL) offers a more data-efficient solution capable of bridging this gap, yet
its application has been constrained by a critical data bottleneck: existing RL
datasets are orders of magnitude smaller and less diverse than web-scale
pre-training corpora. To address this, we introduce the Webscale-RL pipeline, a
scalable data engine that systematically converts large-scale pre-training
documents into millions of diverse, verifiable question-answer pairs for RL.
Using this pipeline, we construct the Webscale-RL dataset, containing 1.2
million examples across more than 9 domains. Our experiments show that the
model trained on this dataset significantly outperforms continual pretraining
and strong data refinement baselines across a suite of benchmarks. Notably, RL
training with our dataset proves substantially more efficient, achieving the
performance of continual pre-training with up to 100times fewer tokens. Our
work presents a viable path toward scaling RL to pre-training levels, enabling
more capable and efficient language models.