OpenNovelty.org

Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
LLM data pipelineReinforcement learning
Abstract:

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 \textbf{\texttt{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 \textbf{\texttt{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 100×\times fewer tokens. Our work presents a viable path toward scaling RL to pre-training levels, enabling more capable and efficient language models.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces a webscale data pipeline and dataset for reinforcement learning in language model training, converting large-scale pre-training documents into verifiable question-answer pairs. Within the taxonomy, it occupies the 'Webscale Data Pipeline Construction' leaf under 'Data Construction and Curation', where it is currently the sole representative among fifty surveyed papers. This unique position suggests the work addresses a relatively sparse research direction focused specifically on automated, large-scale conversion of pre-training corpora into RL-ready formats, distinguishing it from neighboring efforts in domain-specific dataset creation or sample selection strategies.

The broader 'Data Construction and Curation' branch contains five leaves covering domain-specific datasets, sample selection, synthetic data generation, and vision-language construction. Neighboring branches include 'RL Algorithm Design' (policy optimization, reward modeling) and 'Reasoning Capability Enhancement' (prolonged training, inference scaling). The taxonomy's scope notes clarify that webscale pipeline work excludes domain-specific curation and sample filtering, which belong to sibling leaves. The paper's focus on systematic conversion infrastructure positions it at the intersection of data engineering and RL training, complementing algorithmic advances in policy optimization and reasoning enhancement explored elsewhere in the field.

Among twenty-five candidates examined through semantic search and citation expansion, the contribution-level analysis reveals varied novelty signals. The webscale data pipeline contribution examined six candidates with zero refutations, suggesting limited direct prior work on automated conversion infrastructure at this scale. The dataset contribution examined ten candidates, also with zero refutations, indicating the specific combination of scale and domain diversity may be novel. However, the empirical demonstration of RL efficiency examined nine candidates and found one refutable match, suggesting that claims about RL's token efficiency versus continual pre-training may overlap with existing comparative studies in the limited search scope.

Based on this analysis of thirty candidates from top-K semantic matches, the work appears to occupy a genuinely sparse research direction in webscale data pipeline construction, though the empirical efficiency claims show some overlap with prior comparative studies. The limited search scope means these findings reflect the most semantically similar work rather than an exhaustive field survey, and the single-paper leaf status may shift as the field evolves and more webscale data engineering efforts emerge.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
25
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Scaling reinforcement learning data for language model training. The field has evolved into several interconnected branches that address different facets of this challenge. RL Algorithm Design and Optimization focuses on refining policy gradient methods, reward modeling, and training stability, with works like ReST[1] and SRPO[37] exploring iterative refinement and preference optimization. Reasoning Capability Enhancement targets mathematical problem-solving and multi-step inference, exemplified by efforts such as Big Math[18] and approaches that leverage test-time computation scaling. Data Construction and Curation encompasses strategies for building large-scale training corpora, including synthetic data generation, filtering pipelines, and quality control mechanisms seen in Kimi[2] and ProRL[3]. Systems Infrastructure and Scalability addresses distributed training, efficient sampling, and computational resource management. Domain Applications and Specialization apply RL techniques to areas like code generation (SWE RL[24]), vision-language models (VLM R1[15]), and interactive agents (ComputerRL[14]). Surveys and Theoretical Frameworks provide broader perspectives on post-training paradigms and the role of RL in modern language models, while Auxiliary Methods and Techniques cover supporting tools such as curriculum learning and self-correction mechanisms. A particularly active line of work contrasts online RL methods that continuously generate and refine data against offline approaches that curate fixed datasets, raising questions about sample efficiency versus exploration breadth. Another tension emerges between verifiable domains like mathematics, where reward signals are clear, and open-ended tasks requiring human or AI feedback (RLAIF vs RLHF[4]). Webscale RL[0] sits within the Data Construction and Curation branch, specifically targeting webscale data pipeline construction. Its emphasis on building infrastructure for massive-scale data generation aligns closely with works like Kimi[2] and ProRL[3], which also prioritize systematic data curation and quality at scale. Compared to Entropy Mechanism[5], which focuses on algorithmic refinement of exploration strategies, Webscale RL[0] appears more concerned with the engineering and operational challenges of sustaining large RL data flows, positioning it as a foundational effort in enabling the broader ecosystem of RL-driven language model training.

Claimed Contributions

Webscale-RL data pipeline

The authors propose an automated pipeline that transforms web-scale pretraining corpora into verifiable question-answer pairs suitable for reinforcement learning. The pipeline includes stages for data filtering, domain classification with persona assignment, QA generation using domain-specific demonstrations, and quality verification to ensure correctness and prevent leakage.

6 retrieved papers
Webscale-RL dataset

The authors build a large-scale RL dataset by applying their pipeline to pretraining corpora, resulting in 1.2 million verifiable QA pairs spanning over nine domains. The dataset is shown to be significantly more diverse than existing large-scale RL datasets and can be scaled to pretraining levels.

10 retrieved papers
Empirical demonstration of RL efficiency and effectiveness

The authors provide experimental evidence that reinforcement learning on their dataset outperforms continual pretraining and data refinement baselines across multiple benchmarks. They demonstrate that RL achieves comparable performance to continual pretraining while using up to two orders of magnitude fewer tokens, establishing a more data-efficient training paradigm.

9 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Webscale-RL data pipeline

The authors propose an automated pipeline that transforms web-scale pretraining corpora into verifiable question-answer pairs suitable for reinforcement learning. The pipeline includes stages for data filtering, domain classification with persona assignment, QA generation using domain-specific demonstrations, and quality verification to ensure correctness and prevent leakage.

Contribution

Webscale-RL dataset

The authors build a large-scale RL dataset by applying their pipeline to pretraining corpora, resulting in 1.2 million verifiable QA pairs spanning over nine domains. The dataset is shown to be significantly more diverse than existing large-scale RL datasets and can be scaled to pretraining levels.

Contribution

Empirical demonstration of RL efficiency and effectiveness

The authors provide experimental evidence that reinforcement learning on their dataset outperforms continual pretraining and data refinement baselines across multiple benchmarks. They demonstrate that RL achieves comparable performance to continual pretraining while using up to two orders of magnitude fewer tokens, establishing a more data-efficient training paradigm.

Table of Contents