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SFTMill is an open-source tool that enables off-policy distillation of large language models using an OpenAI-compatible endpoint, allowing users to create training datasets from behavioral goals defined in a curriculum.
NeMo Data Designer (NDD) is an open-source, extensible framework for generating multimodal synthetic data, designed for intuitive use and reproducibility in AI model development.
SCTab-Diff is a semantics-consistent tabular diffusion framework that uses weak semantic priors to generate high-fidelity synthetic tabular data, improving distributional fidelity and semantic consistency over existing methods.
SelfGraphRAG introduces a framework that generates synthetic question-answer pairs from knowledge graphs to address the supervision gap in graph-based retrieval-augmented generation, enhancing retrieval precision and reasoning performance.
The paper proposes MR-Traj, a multi-resolution diffusion framework for synthetic trajectory generation in urban systems, which captures complex spatial-temporal dependencies at multiple resolutions and improves performance in fine-grained mobility modeling for downstream tasks.
Proposes a Task-Conditioned Synthetic Data Generation (TCSDG) algorithm combining a Bayesian Network generator with a transformer-based tabular foundation model to improve ML performance in agricultural prediction tasks, showing consistent improvements over benchmarks.
MMDiff extends frozen diffusion transformers into multi-modal generative systems using lightweight decoders, achieving significant improvements in semantic segmentation and other perceptual tasks through multi-timestep feature fusion.