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.
LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究所）；FLAIR, University of Oxford（牛津大学FLAIR）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence
Leverhulme Centre for the Future of Intelligence（勒沃休姆未来智能中心）；University of Cambridge（剑桥大学）；Valencian Research Institute for Artificial Intelligence（瓦伦西亚人工智能研究院）；Universitat Politècnica de València（瓦伦西亚理工大学）
CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL
Tsinghua University（清华大学）；Beihang University（北京航空航天大学）；Southern University of Science and Technology（南方科技大学）；Renmin University of China（中国人民大学）；Shanghai University of Finance and Economics（上海财经大学）
TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models
Electronics and Telecommunications Research Institute（电信研究所）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Hugging Face；Ant Group（蚂蚁集团）
How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks
University of Michigan（密歇根大学）；Stanford University（斯坦福大学）；All Hands AI；Google Deepmind（谷歌DeepMind）；Microsoft AI（微软AI）；Massachusetts Institute of Technology（麻省理工学院）
