data mixture

FoundIR-v2: Optimizing Pre-Training Data Mixtures for Image Restoration Foundation Model CVPR 2026 Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives EACL 2026 TReX: Tokenizer Regression for Optimal Data Mixture EACL 2026 Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws ICLR 2025 AutoMixer: Checkpoint Artifacts as Automatic Data Mixers ACL 2025 Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls EMNLP 2025 Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training ICML 2025 Enhancing Low-Resource Language and Instruction Following Capabilities of Audio Language Models Interspeech 2025 MixMin: Finding Data Mixtures via Convex Minimization ICML 2025 Optimizing Pre-Training Data Mixtures with Mixtures of Data Expert Models ACL 2025 RegMix: Data Mixture as Regression for Language Model Pre-training ICLR 2025 Towards Effective and Efficient Continual Pre-training of Large Language Models ACL 2025 CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models EMNLP 2024 Data Engineering for Scaling Language Models to 128K Context ICML 2024 Data Mixture Inference Attack: BPE Tokenizers Reveal Training Data Compositions NeurIPS 2024 SMART: Submodular Data Mixture Strategy for Instruction Tuning ACL 2024