{"total":157,"rows":[{"id":"2406.00515","title":"A Survey on Large Language Models for Code Generation","created":"2024-06-01","cited_by_count":658,"area":null},{"id":"2503.24235","title":"A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?","created":"2025-03-31","cited_by_count":223,"area":"lr"},{"id":"2405.17247","title":"An Introduction to Vision-Language Modeling","created":"2024-05-27","cited_by_count":178,"area":"mllm"},{"id":"2506.01937","title":"RewardBench 2: Advancing Reward Model Evaluation","created":"2025-06-02","cited_by_count":150,"area":"alignment"},{"id":"2410.02660","title":"How to Train Long-Context Language Models (Effectively)","created":"2024-10-03","cited_by_count":146,"area":null},{"id":"2406.06046","title":"MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models","created":"2024-06-10","cited_by_count":100,"area":null},{"id":"2502.10341","title":"Organize the Web: Constructing Domains Enhances Pre-Training Data Curation","created":"2025-02-14","cited_by_count":76,"area":null},{"id":"2504.13161","title":"Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training","created":"2025-04-17","cited_by_count":66,"area":null},{"id":"2410.16713","title":"Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World","created":"2024-10-22","cited_by_count":52,"area":null},{"id":"2507.09404","title":"Scaling Laws for Optimal Data Mixtures","created":"2025-07-12","cited_by_count":52,"area":null},{"id":"2407.06645","title":"Entropy Law: The Story Behind Data Compression and LLM Performance","created":"2024-07-09","cited_by_count":44,"area":null},{"id":"2409.06857","title":"What is the Role of Small Models in the LLM Era: A Survey","created":"2024-09-10","cited_by_count":41,"area":null},{"id":"2505.05427","title":"Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data","created":"2025-05-08","cited_by_count":38,"area":null},{"id":"2409.17115","title":"Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale","created":"2024-09-25","cited_by_count":37,"area":null},{"id":"2502.09650","title":"Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples","created":"2025-02-11","cited_by_count":37,"area":"alignment"},{"id":"2508.21148","title":"A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers","created":"2025-08-28","cited_by_count":37,"area":"agents"},{"id":"2412.02980","title":"Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models","created":"2024-12-04","cited_by_count":35,"area":"synth"},{"id":"2502.10361","title":"Enhancing Multilingual LLM Pretraining with Model-Based Data Selection","created":"2025-02-14","cited_by_count":35,"area":null},{"id":"2503.01807","title":"Large-Scale Data Selection for Instruction Tuning","created":"2025-03-03","cited_by_count":35,"area":null},{"id":"2409.16986","title":"Harnessing Diversity for Important Data Selection in Pretraining Large Language Models","created":"2024-09-25","cited_by_count":34,"area":null},{"id":"2506.04689","title":"Recycling the Web: A Method to Enhance Pre-training Data Quality and Quantity for Language Models","created":"2025-06-05","cited_by_count":31,"area":null},{"id":"2407.15235","title":"TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data","created":"2024-07-21","cited_by_count":30,"area":null},{"id":"2408.02085","title":"Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models","created":"2024-08-04","cited_by_count":29,"area":"alignment"},{"id":"2505.19147","title":"Shifting AI Efficiency From Model-Centric to Data-Centric Compression","created":"2025-05-25","cited_by_count":29,"area":"mllm"},{"id":"2411.12882","title":"ProSec: Fortifying Code LLMs with Proactive Security Alignment","created":"2024-11-19","cited_by_count":25,"area":"alignment"},{"id":"2512.16676","title":"DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI","created":"2025-12-18","cited_by_count":23,"area":null},{"id":"2501.01956","title":"Metadata Conditioning Accelerates Language Model Pre-training","created":"2025-01-03","cited_by_count":22,"area":null},{"id":"2412.03881","title":"Weak-to-Strong Generalization Through the Data-Centric Lens","created":"2024-12-05","cited_by_count":21,"area":"alignment"},{"id":"2503.00808","title":"Predictive Data Selection: The Data That Predicts Is the Data That Teaches","created":"2025-03-02","cited_by_count":21,"area":null},{"id":"2510.23081","title":"A Survey on LLM Mid-Training","created":"2025-10-27","cited_by_count":21,"area":null},{"id":"2602.05400","title":"OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration","created":"2026-02-05","cited_by_count":21,"area":null},{"id":"2504.14194","title":"Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models","created":"2025-04-19","cited_by_count":19,"area":null},{"id":"2507.12466","title":"Language Models Improve When Pretraining Data Matches Target Tasks","created":"2025-07-16","cited_by_count":19,"area":null},{"id":"2507.03253","title":"RefineX: Learning to Refine Pre-training Data at Scale from Expert-Guided Programs","created":"2025-07-04","cited_by_count":17,"area":null},{"id":"2502.06733","title":"Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining","created":"2025-02-10","cited_by_count":16,"area":null},{"id":"2504.16511","title":"QuaDMix: Quality-Diversity Balanced Data Selection for Efficient LLM Pretraining","created":"2025-04-23","cited_by_count":16,"area":null},{"id":"2408.08310","title":"ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws","created":"2024-08-15","cited_by_count":15,"area":null},{"id":"2510.16882","title":"Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning","created":"2025-10-19","cited_by_count":15,"area":null},{"id":"2503.09202","title":"Token Weighting for Long-Range Language Modeling","created":"2025-03-12","cited_by_count":14,"area":null},{"id":"2511.18903","title":"How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining","created":"2025-11-24","cited_by_count":14,"area":null},{"id":"2409.19458","title":"Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach","created":"2024-09-28","cited_by_count":13,"area":null},{"id":"2502.04380","title":"Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data","created":"2025-02-05","cited_by_count":13,"area":null},{"id":"2502.14709","title":"Group-Level Data Selection for Efficient Pretraining","created":"2025-02-20","cited_by_count":13,"area":null},{"id":"2410.16208","title":"Compute-Constrained Data Selection","created":"2024-10-21","cited_by_count":12,"area":null},{"id":"2502.10673","title":"Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs","created":"2025-02-15","cited_by_count":12,"area":"rag"},{"id":"2506.02096","title":"SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis","created":"2025-06-02","cited_by_count":12,"area":"lr"},{"id":"2408.01323","title":"FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only","created":"2024-08-02","cited_by_count":11,"area":null},{"id":"2503.00743","title":"Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models","created":"2025-03-02","cited_by_count":11,"area":"mllm"},{"id":"2410.08102","title":"Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration","created":"2024-10-10","cited_by_count":10,"area":null},{"id":"2503.01506","title":"SampleMix: A Sample-wise Pre-training Data Mixing Strategey by Coordinating Data Quality and Diversity","created":"2025-03-03","cited_by_count":10,"area":null},{"id":"2505.19947","title":"MESS+: Dynamically Learned Inference-Time LLM Routing in Model Zoos with Service Level Guarantees","created":"2025-05-26","cited_by_count":9,"area":null},{"id":"2502.06244","title":"PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts","created":"2025-02-10","cited_by_count":8,"area":null},{"id":"2504.06536","title":"Lugha-Llama: Adapting Large Language Models for African Languages","created":"2025-04-09","cited_by_count":8,"area":null},{"id":"2510.06499","title":"Webscale-RL: Automated Data Pipeline for Scaling RL Data to Pretraining Levels","created":"2025-10-07","cited_by_count":8,"area":"offlinerl"},{"id":"2510.25207","title":"Selective Learning for Deep Time Series Forecasting","created":"2025-10-29","cited_by_count":8,"area":null},{"id":"2507.16178","title":"LLM Data Selection and Utilization via Dynamic Bi-level Optimization","created":"2025-07-22","cited_by_count":7,"area":null},{"id":"2410.05639","title":"DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models","created":"2024-10-08","cited_by_count":6,"area":null},{"id":"2501.07314","title":"FinerWeb-10BT: Refining Web Data with LLM-Based Line-Level Filtering","created":"2025-01-13","cited_by_count":6,"area":null},{"id":"2502.19279","title":"CritiQ: Mining Data Quality Criteria from Human Preferences","created":"2025-02-26","cited_by_count":6,"area":"lr"},{"id":"2503.02359","title":"Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm","created":"2025-03-04","cited_by_count":6,"area":null},{"id":"2505.16322","title":"AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners","created":"2025-05-22","cited_by_count":6,"area":"lr"},{"id":"2506.10403","title":"Time To Impeach LLM-as-a-Judge: Programs are the Future of Evaluation","created":"2025-06-12","cited_by_count":6,"area":null},{"id":"2510.10681","title":"RePro: Training Language Models to Faithfully Recycle the Web for Pretraining","created":"2025-10-12","cited_by_count":6,"area":null},{"id":"2510.16657","title":"Escaping Model Collapse via Synthetic Data Verification: Near-term Improvements and Long-term Convergence","created":"2025-10-18","cited_by_count":6,"area":"synth"},{"id":"2601.10064","title":"Long-Chain Reasoning Distillation via Adaptive Prefix Alignment","created":"2026-01-15","cited_by_count":6,"area":"lr"},{"id":"2602.12414","title":"propella-1: Multi-Property Document Annotation for LLM Data Curation at Scale","created":"2026-02-12","cited_by_count":6,"area":null},{"id":"2604.00536","title":"Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation","created":"2026-04-01","cited_by_count":6,"area":"synth"},{"id":"2503.21383","title":"Controlling Large Language Model with Latent Actions","created":"2025-03-27","cited_by_count":5,"area":null},{"id":"2504.20644","title":"Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection","created":"2025-04-29","cited_by_count":5,"area":null},{"id":"2505.07293","title":"AttentionInfluence: Adopting Attention Head Influence for Weak-to-Strong Pretraining Data Selection","created":"2025-05-12","cited_by_count":5,"area":"lr"},{"id":"2507.01785","title":"MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining","created":"2025-07-02","cited_by_count":5,"area":null},{"id":"2507.09424","title":"DATE-LM: Benchmarking Data Attribution Evaluation for Large Language Models","created":"2025-07-12","cited_by_count":5,"area":null},{"id":"2512.24265","title":"Joint Selection for Large-Scale Pre-Training Data via Policy Gradient-based Mask Learning","created":"2025-12-30","cited_by_count":5,"area":null},{"id":"2501.02432","title":"Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding","created":"2025-01-05","cited_by_count":4,"area":null},{"id":"2502.05551","title":"FRAME: Boosting LLMs with A Four-Quadrant Multi-Stage Pretraining Strategy","created":"2025-02-08","cited_by_count":4,"area":null},{"id":"2502.12022","title":"Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving","created":"2025-02-17","cited_by_count":4,"area":"lr"},{"id":"2503.14917","title":"MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models","created":"2025-03-19","cited_by_count":4,"area":null},{"id":"2505.18458","title":"A Survey of LLM $\\times$ DATA","created":"2025-05-24","cited_by_count":4,"area":"rag"},{"id":"2507.09205","title":"From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan","created":"2025-07-12","cited_by_count":4,"area":"moe"},{"id":"2510.25804","title":"Beyond Length: Quantifying Long-Range Information for Long-Context LLM Pretraining Data","created":"2025-10-29","cited_by_count":4,"area":"lr"},{"id":"2602.09003","title":"Data Science and Technology Towards AGI Part I: Tiered Data Management","created":"2026-02-09","cited_by_count":4,"area":null},{"id":"2409.14705","title":"Target-Aware Language Modeling via Granular Data Sampling","created":"2024-09-23","cited_by_count":3,"area":null},{"id":"2410.10901","title":"3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection","created":"2024-10-13","cited_by_count":3,"area":null},{"id":"2501.06708","title":"Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights","created":"2025-01-12","cited_by_count":3,"area":null},{"id":"2503.10789","title":"Data Caricatures: On the Representation of African American Language in Pretraining Corpora","created":"2025-03-13","cited_by_count":3,"area":null},{"id":"2505.05327","title":"RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection","created":"2025-05-08","cited_by_count":3,"area":null},{"id":"2505.18557","title":"TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation","created":"2025-05-24","cited_by_count":3,"area":null},{"id":"2505.22131","title":"Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection","created":"2025-05-28","cited_by_count":3,"area":"lr"},{"id":"2508.01326","title":"Large-Scale Diverse Synthesis for Mid-Training","created":"2025-08-02","cited_by_count":3,"area":null},{"id":"2510.06048","title":"BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining","created":"2025-10-07","cited_by_count":3,"area":null},{"id":"2510.13928","title":"LLMs Can Get \"Brain Rot\": A Pilot Study on Twitter/X","created":"2025-10-15","cited_by_count":3,"area":null},{"id":"2512.13279","title":"AIR: Post-training Data Selection for Reasoning via Attention Head Influence","created":"2025-12-15","cited_by_count":3,"area":"lr"},{"id":"2604.04204","title":"How Does \"English (US)\" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline","created":"2026-04-05","cited_by_count":3,"area":null},{"id":"2605.05227","title":"Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods","created":"2026-04-19","cited_by_count":3,"area":"lr"},{"id":"2605.22389","title":"Unified Data Selection for LLM Reasoning","created":"2026-05-21","cited_by_count":3,"area":"lr"},{"id":"2605.30288","title":"MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection","created":"2026-05-28","cited_by_count":3,"area":null},{"id":"2605.30334","title":"Demystifying Data Organization for Enhanced LLM Training","created":"2026-05-28","cited_by_count":3,"area":null},{"id":"2607.28109","title":"Beyond Rephrasing: Book-Level Organization Improves Synthetic Textbook Data for Mid-Training","created":"2026-07-30","cited_by_count":3,"area":null},{"id":"2410.09817","title":"Reverse Modeling in Large Language Models","created":"2024-10-13","cited_by_count":2,"area":null},{"id":"2505.23114","title":"Alignment Data Map for Efficient Preference Data Selection and Diagnosis","created":"2025-05-29","cited_by_count":2,"area":"alignment"}],"next_cursor":"100"}