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The paper introduces OPDLM, a method that transforms autoregressive language models into diffusion language models via on-policy distillation, requiring 15x to 7000x fewer training tokens while retaining knowledge from the original model.
This paper presents a data-efficient anatomy-aware benchmark for cardiac pathology prediction on the ACDC MRI dataset, showing that under limited labels, anatomical representation matters more than model complexity.
This paper introduces BrainSimSiam, a lightweight self-supervised framework using siamese networks to learn robust fMRI representations from positive-only pairs, achieving strong performance on downstream tasks even with limited data.
This paper proposes a retrieval-based approach for multi-label legal annotation that uses frozen embedding models to retrieve labels via k-nearest neighbors, achieving competitive accuracy, high data efficiency, and eliminating label hallucination by design.
FrameSkip is a data-layer frame selection method that improves Vision-Language-Action (VLA) policy training by prioritizing high-importance frames based on action variation and visual-coherence metrics, achieving a macro-average success rate of 76.15% across three benchmarks while using only 20% of unique frames.
This paper introduces 'Hint Tuning,' a data-efficient method that reduces token usage in reasoning models by calibrating reasoning depth based on problem difficulty. It achieves significant token reduction (24–66%) on models like Qwen3-Thinking and DeepSeek-R1-Distill using only 1K self-annotated samples.