The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking. There are two major gaps in existing benchmarks: (i) the lack of standardized, process-aware evaluation that captures instruction adherence and process fidelity, and (ii) the scarcity of accurately labeled training data. To bridge these gaps, we introduce DARE-bench, a benchmark designed for machine learning modeling and data science instruction following. Unlike many existing benchmarks that rely on human- or model-based judges, all tasks in DARE-bench have verifiable ground truth, ensuring objective and reproducible evaluation. To cover a broad range of tasks and support agentic tools, DARE-bench consists of 6,300 Kaggle-derived tasks and provides both large-scale training data and evaluation sets. Extensive evaluations show that even highly capable models such as gpt-o4-mini struggle to achieve good performance, especially in machine learning modeling tasks. Using DARE-bench training tasks for fine-tuning can substantially improve model performance. For example, supervised fine-tuning boosts Qwen3-32B's accuracy by 1.83x and reinforcement learning boosts Qwen3-4B's accuracy by more than 8x. These significant improvements verify the importance of DARE-bench both as an accurate evaluation benchmark and critical training data.
DARE: Diffusion Language Model Activation Reuse for Efficient Inference
Chandra Family Department of Electrical and Computer Engineering（查德拉家族电子与计算机工程系）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Cornell University（康奈尔大学）
Benchmarking Complex Instruction-Following with Multiple Constraints Composition
Tsinghua University（清华大学）；University of Electronic Science and Technology of China（电子科技大学）；Zhipu AI（智谱AI）；China University of Geosciences（中国地质大学）；Central China Normal University（华中师范大学）
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
Hebrew University（希伯来大学）；Google Research（谷歌研究院）；UCLA（加州大学洛杉矶分校）；Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；UCSB（加州大学圣塔芭芭拉分校）；Stanford（斯坦福大学）；LAION
Rapidly Developing High-quality Instruction Data and Evaluation Benchmark for Large Language Models with Minimal Human Effort: A Case Study on Japanese
LLMs can be easily Confused by Instructional Distractions
IPAI, Seoul National University（IPAI，首尔国立大学）；LG AI Research（LG人工智能研究所）；Dept. of ECE, Seoul National University（电子工程系，首尔国立大学）；SNU-LG AI Research Center（SNU-LG人工智能研究所）
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
HKUST（香港科技大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）；Ohio State University（俄亥俄州立大学）；AIWaves Inc.（AIWaves公司）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, Chinese Academy of Sciences（中国科学院人工智能学院）；Peking University（北京大学）；Beijing Foreign Studies University（北京外国语大学）；University of Manchester（曼彻斯特大学）；University of Waterloo（滑铁卢大学）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）
IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation
The Conversational Artificial Intelligence (CoAI) Group, Tsinghua University（清华大学对话人工智能（CoAI）小组）；Zhipu AI（智谱AI）；University of Electronic Science and Technology of China（电子科技大学）
IndicIFEval: A Benchmark for Verifiable Instruction-Following Evaluation in 14 Indic Languages
Nilekani Centre at AI4Bharat（AI4Bharat的Nilekani中心）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；IT University of Copenhagen（丹麦技术大学）；Microsoft（微软）
VCIFBench: Evaluating Complex Instruction Following for Video Understanding
School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University（知识驱动人机智能工程研究中心，吉林大学）；International Center of Future Science, Jilin University（未来科学国际中心，吉林大学）
