Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems
arXiv:2603.07779v1 Announce Type: new Abstract: Training next-generation code generation models requires high-quality datasets, yet existing datasets face difficulty imbalance, format inconsistency, and data quality problems. We address these challenges through systematic data processing and difficulty scaling. We introduce a four-stage Data Processing Framework encompassing collection, processing, filtering, and verification, incorporating Automatic Difficulty Filtering via an LLM-based predict-calibrate-select framework that leverages multi-dimensional difficulty metrics across five weighted dimensions to retain challenging problems while removing simplistic ones. The resulting MicroCoder dataset comprises tens of thousands of curated real competitive programming problems from diverse platforms, emphasizing recency and difficulty. Evaluations on strictly unseen LiveCodeBench demonstrate that MicroCoder achieves 3x larger performance gains within 300 training steps compared to widely
arXiv:2603.07779v1 Announce Type: new Abstract: Training next-generation code generation models requires high-quality datasets, yet existing datasets face difficulty imbalance, format inconsistency, and data quality problems. We address these challenges through systematic data processing and difficulty scaling. We introduce a four-stage Data Processing Framework encompassing collection, processing, filtering, and verification, incorporating Automatic Difficulty Filtering via an LLM-based predict-calibrate-select framework that leverages multi-dimensional difficulty metrics across five weighted dimensions to retain challenging problems while removing simplistic ones. The resulting MicroCoder dataset comprises tens of thousands of curated real competitive programming problems from diverse platforms, emphasizing recency and difficulty. Evaluations on strictly unseen LiveCodeBench demonstrate that MicroCoder achieves 3x larger performance gains within 300 training steps compared to widely-used baseline datasets of comparable size, with consistent advantages under both GRPO and its variant training algorithms. The MicroCoder dataset delivers obvious improvements on medium and hard problems across different model sizes, achieving up to 17.2% relative gains in overall performance where model capabilities are most stretched. These results validate that difficulty-aware data curation improves model performance on challenging tasks, providing multiple insights for dataset creation in code generation.
Executive Summary
This article presents a novel approach to addressing the challenges of training next-generation code generation models, namely difficulty imbalance, format inconsistency, and data quality problems. The authors introduce a four-stage Data Processing Framework and Automatic Difficulty Filtering via an LLM-based predict-calibrate-select framework, resulting in the MicroCoder dataset. Evaluations demonstrate significant performance gains on challenging problems compared to widely-used baseline datasets. The study provides insights for dataset creation in code generation and validates the importance of difficulty-aware data curation. The authors' framework and dataset have the potential to improve the performance of code generation models, particularly on medium and hard problems. The results have significant implications for the development of more effective code generation models, which are crucial for various applications, including software development and artificial intelligence.
Key Points
- ▸ Introduction of a four-stage Data Processing Framework for systematic data processing
- ▸ Development of Automatic Difficulty Filtering via an LLM-based predict-calibrate-select framework
- ▸ Creation of the MicroCoder dataset, comprising tens of thousands of curated real competitive programming problems
- ▸ Evaluations demonstrating significant performance gains on challenging problems compared to baseline datasets
Merits
Scalability and Flexibility
The proposed framework and dataset are scalable and flexible, enabling the efficient processing and filtering of large datasets, and the creation of datasets tailored to specific model architectures and problem domains.
Difficulty-Aware Data Curation
The study highlights the importance of difficulty-aware data curation in improving model performance on challenging tasks, providing insights for dataset creation in code generation.
Demerits
Data Quality and Consistency
The authors acknowledge the potential issues with data quality and consistency in the MicroCoder dataset, which may impact model performance and reliability.
Limited Generalizability
The study's focus on competitive programming problems and the specific model architectures used may limit the generalizability of the results to other domains and problem types.
Expert Commentary
The study presents a novel approach to addressing the challenges of training next-generation code generation models. The authors' framework and dataset have the potential to improve the performance of code generation models, particularly on medium and hard problems. However, the study's limitations, including potential issues with data quality and consistency, and limited generalizability, highlight the need for further research and evaluation. The findings of the study have significant implications for the development of more effective code generation models, and the study's insights on difficulty-aware data curation and model training are particularly valuable.
Recommendations
- ✓ Further research and evaluation are needed to address the limitations of the study, including potential issues with data quality and consistency, and limited generalizability.
- ✓ The development of more effective code generation models, particularly on medium and hard problems, should prioritize difficulty-aware data curation and model training.