Knowledge distillation (KD) is a key technique for compressing large language models (LLMs), yet it faces challenges stemming from the teacher–student capacity gap. While existing KD methods address these challenges either by mixing teacher and student distributions in the distillation target or by using curriculum learning to sequence training from easy to hard examples, they typically design these two strategies independently, missing the opportunity for synergistic co-design. To bridge this gap, we propose Calibrated Progressive Distillation (CPD), a white-box KD framework that co-designs curriculum scheduling and target mixing through a unified difficulty-aware principle. CPD uses a difficulty profile to select epoch-specific subsets that ensure a uniform increase in average difficulty, adapting to the dataset’s intrinsic hardness structure. Simultaneously, the mixing coefficient in the distillation target and the distillation temperature are synchronized with this progression, gradually shifting supervision from teacher-dominated to student-informed signals as training advances. Theoretically, CPD ensures bounded gradients and induces an implicit attention shift from easy to hard samples. Empirically, CPD consistently outperforms advanced KD methods across diverse tasks, while reducing training runtime by over 10%. Our work demonstrates that aligning data scheduling with distillation signal design is crucial for effective and efficient LLM distillation.

Supervised post-training is essential for refining Large Language Models (LLMs), yet its effectiveness relies heavily on strategic data curation. Traditional Curriculum Learning (CL) strategies often fail to account for the evolving proficiency of the learner, relying instead on static, single dimensional metrics. We propose EVO-Curate, a dynamic data curation framework that synchronizes sample complexity with the maturing capacity of the LLM. EVO-Curate employs an Adaptive Dynamics Measurer to synthesize instantaneous difficulty and historical variability into a multidimensional utility score. To maintain representational diversity, we introduce an Evolutionary Sampling Scheduler based on an entropy maximizing mechanism. Empirical evaluations across instruction following, mathematical reasoning, and code generation demonstrate that EVO-Curate consistently outperforms standard training baselines and traditional CL methods across various architectures and scales. Specifically, our framework achieves relative performance gains of up to about 10% while maintaining manageable computational overhead. These results establish EVO-Curate as a scalable and model agnostic solution for enhancing the efficiency of modern LLM training pipelines.

Large Language Models (LLMs) have shown potential in generating hypothetical documents for query expansion, thereby enhancing information retrieval performance. However, the efficacy of this method is highly dependent on the quality of the generated documents, which often requires complex prompt strategies and the integration of advanced dense retrieval techniques. This can be both costly and computationally intensive. To mitigate these limitations, we explore the use of zero-shot LLM-based query expansion to improve sparse retrieval, particularly for learned sparse retrievers. We introduce a novel fusion ranking framework, Exp4Fuse, which enhances the performance of sparse retrievers through an indirect application of zero-shot LLM-based query expansion. Exp4Fuse operates by simultaneously considering two retrieval routes—one based on the original query and the other on the LLM-augmented query. It then generates two ranked lists using a sparse retriever and fuses them using a modified reciprocal rank fusion method. We conduct extensive evaluations of Exp4Fuse against leading LLM-based query expansion methods and advanced retrieval techniques on three MS MARCO-related datasets and seven low-resource datasets. Experimental results reveal that Exp4Fuse not only surpasses existing LLM-based query expansion methods in enhancing sparse retrievers but also, when combined with advanced sparse retrievers, achieves SOTA results on several benchmarks. This highlights the superior performance and effectiveness of Exp4Fuse in improving query expansion for sparse retrieval.

Large language models (LLMs)-based query expansion for information retrieval augments queries with generated hypothetical documents with LLMs. However, its performance relies heavily on the scale of the language models (LMs), necessitating larger, more advanced LLMs. This approach is costly, computationally intensive, and often has limited accessibility. To address these limitations, we introduce GOLFer - Smaller LMs-Generated Documents Hallucination Filter & Combiner - a novel method leveraging smaller open-source LMs for query expansion. GOLFer comprises two modules: a hallucination filter and a documents combiner. The former detects and removes non-factual and inconsistent sentences in generated documents, a common issue with smaller LMs, while the latter combines the filtered content with the query using a weight vector to balance their influence. We evaluate GOLFer alongside dominant LLMs-based query expansion methods on three web search and ten low-resource datasets. Experimental results demonstrate that GOLFer consistently outperforms other methods using smaller LMs, and maintains competitive performance against methods using large-size LLMs, demonstrating its effectiveness.

Knowledge distillation (KD) enables the compression of large language models (LLMs) by transferring knowledge from a high-capacity teacher model to a resource-efficient student model, maintaining competitive performance for tasks such as instruction following. However, conventional white-box KD methods often suffer from training-inference mismatches and suboptimal performance due to the asymmetric nature of Kullback-Leibler divergence (KLD) and reliance on computationally expensive student-generated outputs. To address these challenges, we propose Least-to-Most Prompting Knowledge Distillation (L2M-KD), a novel white-box KD method grounded in curriculum learning (CL) and adaptive loss design. L2M-KD employs a two-pronged approach: (1) a CL strategy that ranks training samples by difficulty using Rouge-L scores, partitioning them into easy-to-hard subsets across multiple stages, and (2) an adaptive KD loss that transitions from KLD to skew KLD, dynamically adjusting teacher guidance to mitigate mode-averaging and over-smoothing. Extensive experiments on instruction-following tasks demonstrate that L2M-KD outperforms existing white-box KD methods, achieving superior student model performance with reduced computational overhead by leveraging ground-truth outputs exclusively. Our findings underscore the efficacy of difficulty-aware training and adaptive teacher guidance, offering a computationally efficient and robust approach to LLM compression.