Journal of Computer Technology and Software

Vol. 5 No. 3 (2025)
Articles

Unified Optimization Framework for Large Language Models via Multi-Objective Alignment and Adaptive Knowledge Distillation

pdf
  • Percival Huxley

Published 2026-03-30

How to Cite

Huxley, P. (2026). Unified Optimization Framework for Large Language Models via Multi-Objective Alignment and Adaptive Knowledge Distillation. Journal of Computer Technology and Software, 5(3). Retrieved from https://ashpress.org/index.php/jcts/article/view/253
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Large Language Models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their deployment in real-world systems remains constrained by inefficiencies in training dynamics, domain generalization, and alignment with downstream objectives. This paper proposes a unified optimization framework that integrates multi-objective learning, adaptive knowledge distillation, and distribution-aware fine-tuning to enhance both performance and efficiency of LLMs. The framework jointly optimizes task-specific objectives, representation alignment, and inference efficiency through a coordinated training mechanism. Experimental results demonstrate consistent improvements in accuracy, convergence speed, and robustness across diverse benchmarks, highlighting the effectiveness of the proposed method.

pdf

References

  1. J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” arXiv preprint arXiv:2001.08361, 2020.
  2. J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” in Proc. 56th Annu. Meeting of the Association for Computational Linguistics (ACL), 2018, pp. 328-339.
  3. G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.
  4. T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proc. Int. Conf. Machine Learning (ICML), 2020, pp. 1597-1607.
  5. E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7167-7176.
  6. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, and I. Polosukhin, "Attention is all you need," Advances in Neural Information Processing Systems, vol. 30, 2017.
  7. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," Proceedings of NAACL-HLT, pp. 4171–4186, 2019.
  8. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, and V. Stoyanov, "RoBERTa: A robustly optimized BERT pretraining approach," arXiv preprint arXiv:1907.11692, 2019.
  9. C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, and P. J. Liu, "Exploring the limits of transfer learning with a unified text-to-text transformer," Journal of Machine Learning Research, vol. 21, no. 140, pp. 1–67, 2020.
  10. R. Pope, S. Douglas, A. Chowdhery, J. Devlin, J. Bradbury, J. Heek, and J. Dean, "Efficiently scaling transformer inference," Proceedings of Machine Learning and Systems, vol. 5, pp. 606–624, 2023.
  11. D. Lepikhin, H. Lee, Y. Xu, D. Chen, O. Firat, Y. Huang, and Z. Chen, "GShard: Scaling giant models with conditional computation and automatic sharding," arXiv preprint arXiv:2006.16668, 2020.
  12. L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, and A. Ray, "Training language models to follow instructions with human feedback," Advances in Neural Information Processing Systems, 2022.
  13. Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, and J. Kaplan, "Constitutional AI: Harmlessness from AI feedback," arXiv preprint arXiv:2212.08073, 2022.
  14. J. Yang, S. Sun, Y. Wang, Y. Wang, X. Yang, and C. Zhang, "Semantic alignment and output constrained generation for reliable LLM-based classification," 2026.
  15. C. Wen, A. Zhu, R. Long, H. Huang, J. Jiang, and C. S. Lee, "CalibJudge: Calibrated LLM-as-a-judge for multilingual RAG with uncertainty-aware scoring," 2026.
  16. C. Shao, Y. Zi, Y. Deng, H. Liu, C. Zhang, and Y. Ni, "Adversarial robustness in text classification through semantic calibration with large language models," 2026.
  17. Y. Luan, "Iterative self-questioning supervision with semantic calibration for stable reasoning chains in large language models," 2026.
  18. Y. Li, Y. Tang, K. Wu, Y. Yang, Y. Li, and Y. Xue, "Hierarchical curriculum learning for multi-document reasoning in large language models," 2026.
  19. K. Gao, H. Zhu, R. Liu, J. Li, X. Yan, and Y. Hu, "Contextual trust evaluation for robust coordination in large language model multi-agent systems," 2025.
  20. J. Chen, F. Wang, T. Guan, Y. Ma, L. Yang, and Y. Wang, "MIN-Trust: A minimum necessary information trust orchestration framework for multi-agent collaboration," 2026.
  21. F. Wang, Y. Ma, T. Guan, Y. Wang, and J. Chen, "Autonomous learning through self-driven exploration and knowledge structuring for open-world intelligent agents," 2026.
  22. L. Yang, T. Guan, Y. Ma, Z. Li, Z. Fang, and F. Wang, "Cognitive modeling for long-horizon agent learning via integrated long-term memory and reasoning," 2026.
  23. R. Meng, S. Y. Huang, X. Zhang, Z. Huang, K. Zeng, and Y. Yang, "Constraint-consistent skill composition for reliable zero-shot task generalization in LLM agents."
  24. O. Sener and V. Koltun, "Multi-task learning as multi-objective optimization," Advances in Neural Information Processing Systems, vol. 31, 2018.
  25. A. Kendall, Y. Gal, and R. Cipolla, "Multi-task learning using uncertainty to weigh losses," Proceedings of CVPR, pp. 7482–7491, 2018.
  26. Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, and V. Lempitsky, "Domain-adversarial training of neural networks," Journal of Machine Learning Research, vol. 17, no. 59, pp. 1–35, 2016.
  27. M. Long, Y. Cao, J. Wang, and M. Jordan, "Learning transferable features with deep adaptation networks," Proceedings of ICML, pp. 97–105, 2015.
  28. C. Hu, Z. Cheng, D. Wu, Y. Wang, F. Liu, and Z. Qiu, "Structural generalization for microservice routing using graph neural networks," Proceedings of AIAC, pp. 278–282, 2025.
  29. J. Jiang, C. Shao, C. Zhang, N. Lyu, and Y. Ni, "Adaptive AI spatiotemporal modeling with dependency drift awareness for anomaly detection in large-scale clusters," 2025.
  30. C. Chiang, D. Li, R. Ying, Y. Wang, Q. Gan, and J. Li, "Deep learning-based dynamic graph framework for robust corporate financial health risk prediction," Proceedings of ICMLML, pp. 98–105, 2025.
  31. X. Liang, Y. Zhao, M. Chang, R. Zhou, K. Cao, and Y. Zheng, "Spatiotemporal risk representation learning using transformers and graph structure," 2026.
  32. Q. Zhang, "Adaptive resource scheduling in distributed computing via multi-agent reinforcement learning and graph convolutional modeling," Transactions on Computational and Scientific Methods, vol. 4, no. 11, 2024.
  33. N. Lyu, Y. Wang, Z. Cheng, Q. Zhang, and F. Chen, "Multi-objective adaptive rate limiting in microservices using deep reinforcement learning," Proceedings of AIIP, pp. 862–869, 2025.
  34. C. Wang, C. S. Lee, X. Yang, Z. Qiu, and Y. Tang, "Deep reinforcement learning guided by game-theoretic structure for multi-agent resource allocation and scheduling."
  35. H. Feng, Y. Wang, R. Fang, A. Xie, and Y. Wang, "Federated risk discrimination with siamese networks for financial transaction anomaly detection," Proceedings of DECS, pp. 231–236, 2025.
  36. C. Chen, R. Fang, and J. Lai, "Causal representation learning for robust and interpretable audit risk identification in financial systems," Proceedings of ICEMME, 2026.
  37. N. Chen, S. Sun, Y. Wang, Z. Li, A. Zhu, and Y. Lu, "Few-shot financial fraud detection using meta-learning and large language models," Proceedings of CSMT, pp. 822–826, 2025.
  38. X. Yang, S. Sun, Y. Li, Y. Xing, M. Wang, and Y. Wang, "CaliCausalRank: Calibrated multi-objective ad ranking with robust counterfactual utility optimization," arXiv preprint arXiv:2602.18786, 2026.
  39. Q. Liu, Y. Zhang, S. Chen, Z. Liu, Y. Xu, and H. Cui, "Uncertainty-aware marketing attribution inference and budget decision-making with intelligent agents," 2026.
  40. X. Song, Y. Huang, J. Guo, Y. Liu, and Y. Luan, "Multi-scale feature fusion and graph neural network integration for text classification with large language models," arXiv preprint arXiv:2511.05752, 2025.
  41. X. Song, Y. Liu, Y. Luan, J. Guo, and X. Guo, "Controllable abstraction in summary generation for large language models via prompt engineering," arXiv preprint arXiv:2510.15436, 2025.
  42. R. Ying, Q. Liu, Y. Wang, and Y. Xiao, "AI-based causal reasoning over knowledge graphs for data-driven enterprise performance analysis," Proceedings of CSMT, pp. 770–775, 2025.
  43. H. Chen, Y. Lu, Y. Wei, J. Lyu, R. Wu, and C. Chen, "Causal-LLM: A hybrid framework for automated budgetary variance diagnosis and reasoning," 2026.
  44. R. Yan, Y. Ou, S. Sun, N. Chen, K. Zhou, and Y. Shu, "DualShiftNet: Joint class-imbalance and distribution-shift aware learning for business risk prediction," 2026.
  45. L. Yan, Q. Wang, and J. Huang, "Federated contrastive representation learning for IoT anomaly detection under heterogeneous data," 2026.
  46. S. Han, H. Mao, and W. J. Dally, "Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding," arXiv preprint arXiv:1510.00149, 2015.
  47. J. Frankle and M. Carbin, "The lottery ticket hypothesis," Proceedings of ICLR, 2019.
  48. T. Dao, D. Fu, S. Ermon, A. Rudra, and C. Ré, "FlashAttention: Fast and memory-efficient exact attention," Advances in Neural Information Processing Systems, vol. 35, 2022.
  49. X. Chen, S. U. Gadgil, and J. Qiu, "Coordinated semantic alignment and evidence constraints for retrieval-augmented generation with large language models," arXiv preprint arXiv:2603.04647, 2026.