This paper conducts an in-depth study on the theoretical foundations and optimization mechanisms of instruction tuning for multi-task generalization. We propose a unified, parameter-efficient tuning framework that integrates instruction embedding modeling, task similarity regularization, and gradient alignment. The goal is to enhance the generalization and robustness of large language models under complex task combinations. Current instruction tuning methods often suffer from representation shift, objective conflict, and gradient interference when handling heterogeneous tasks. To address these issues, we propose a systematic solution from both structural design and optimization perspectives. Methodologically, we introduce semantically aligned instruction encodings to improve representation consistency across tasks. During optimization, we apply gradient projection to reduce inter-task update conflicts and adopt a dynamic weighting strategy based on gradient variation to enhance training stability and coordination. On the theoretical side, we construct an upper bound on generalization error based on Rademacher complexity and KL divergence of distributional shifts. This provides a formal characterization of the performance boundaries in multi-task instruction tuning. We conduct multiple experiments on the Super-NaturalInstructions dataset. The evaluation covers various aspects, including different instruction formulations, generalization to unseen tasks, and robustness under task combinations. Results show that the proposed method outperforms baselines on key metrics. The findings confirm its effectiveness in improving generalization under high task heterogeneity and reducing the risk of conflicts during cross-task learning.

This paper addresses the challenges of high computational cost and slow inference in deploying large language models. It proposes a distillation strategy guided by multiple teacher models. The method constructs several teacher models and integrates their output probability distributions and intermediate semantic features. This guides the student model to learn from multiple sources of knowledge. As a result, the student model gains stronger language understanding and generation ability while maintaining a small parameter size. To achieve this, the paper introduces a weighted output fusion mechanism, a feature alignment loss function, and an entropy-driven dynamic teacher weighting strategy. These components improve the quality and stability of knowledge transfer during distillation. Under multi-teacher guidance, the student model captures semantic information more effectively and demonstrates strong performance across multiple evaluation metrics. In particular, the method shows high consistency in expression, generalization ability, and task adaptability in tasks such as language modeling, text generation, and multi-task learning. The experiments compare the proposed method with several widely adopted distillation approaches. The results further confirm its overall advantages in perplexity, distillation loss, and generation quality. This study provides a feasible technical path for the efficient compression of large-scale language models. It also demonstrates the effectiveness of multi-teacher collaborative mechanisms in complex language modeling tasks.

This paper proposes an enhanced feature pyramid-based segmentation algorithm designed for accurate skin disease lesion segmentation. The method utilizes a multi-level convolutional encoder to extract hierarchical features and introduces a cross-scale enhancement module to strengthen the fusion of multi-scale contextual information. Additionally, a space attention mechanism is applied to refine spatial localization and highlight critical lesion regions. The network architecture is systematically constructed to balance the extraction of fine-grained details and global semantic representations. Extensive experiments on the ISIC 2018 dataset demonstrate that the proposed method achieves superior performance compared to several baseline models, showing notable improvements in mIOU, mDice, and F1-Score metrics. Intuitive visualization of segmentation masks and feature maps further validates the model's ability to accurately capture lesion boundaries and suppress background noise. The effectiveness of the enhanced feature pyramid structure confirms its potential to advance the precision and robustness of skin disease image analysis. Through comprehensive evaluations, the method is shown to offer a reliable and scalable solution for skin lesion segmentation tasks.