Knowledge Distillation for Large Language Models
arXiv:2603.13765v1 Announce Type: new Abstract: We propose a resource-efficient framework for compressing large language models through knowledge distillation, combined with guided chain-of-thought reinforcement learning. Using Qwen 3B as the teacher and Qwen 0.5B as the student, we apply knowledge distillation across English Dolly-15k, Spanish Dolly-15k, and code BugNet and PyTorrent datasets, with hyperparameters tuned in the English setting to optimize student performance. Across tasks, the distilled student retains a substantial portion of the teacher's capability while remaining significantly smaller: 70% to 91% in English, up to 95% in Spanish, and up to 93.5% Rouge-L in code. For coding tasks, integrating chain-of-thought prompting with Group Relative Policy Optimization using CoT-annotated Codeforces data improves reasoning coherence and solution correctness compared to knowledge distillation alone. Post-training 4-bit weight quantization further reduces memory footprint and i
arXiv:2603.13765v1 Announce Type: new Abstract: We propose a resource-efficient framework for compressing large language models through knowledge distillation, combined with guided chain-of-thought reinforcement learning. Using Qwen 3B as the teacher and Qwen 0.5B as the student, we apply knowledge distillation across English Dolly-15k, Spanish Dolly-15k, and code BugNet and PyTorrent datasets, with hyperparameters tuned in the English setting to optimize student performance. Across tasks, the distilled student retains a substantial portion of the teacher's capability while remaining significantly smaller: 70% to 91% in English, up to 95% in Spanish, and up to 93.5% Rouge-L in code. For coding tasks, integrating chain-of-thought prompting with Group Relative Policy Optimization using CoT-annotated Codeforces data improves reasoning coherence and solution correctness compared to knowledge distillation alone. Post-training 4-bit weight quantization further reduces memory footprint and inference latency. These results show that knowledge distillation combined with chain-of-thought guided reinforcement learning can produce compact, efficient models suitable for deployment in resource-constrained settings.
Executive Summary
This article introduces a novel approach to compressing large language models through knowledge distillation and guided chain-of-thought reinforcement learning. The authors demonstrate the efficacy of their method across various language models, including English, Spanish, and code datasets. The proposed framework achieves substantial model compression, retaining up to 95% of the teacher's capability while reducing model size significantly. The integration of chain-of-thought prompting and Group Relative Policy Optimization further enhances reasoning coherence and solution correctness. The findings have significant implications for the deployment of language models in resource-constrained settings, offering a promising solution for efficient model deployment. However, the study's limitations, including the reliance on carefully tuned hyperparameters, warrant further investigation.
Key Points
- ▸ Knowledge distillation is combined with guided chain-of-thought reinforcement learning to compress large language models.
- ▸ The proposed framework achieves substantial model compression, retaining up to 95% of the teacher's capability.
- ▸ Chain-of-thought prompting and Group Relative Policy Optimization enhance reasoning coherence and solution correctness.
Merits
Strength in Model Compression
The proposed framework demonstrates substantial model compression, offering a promising solution for efficient model deployment in resource-constrained settings.
Enhanced Reasoning Coherence
The integration of chain-of-thought prompting and Group Relative Policy Optimization enhances reasoning coherence and solution correctness, making the model more reliable and accurate.
Demerits
Hyperparameter Sensitivity
The study's reliance on carefully tuned hyperparameters may limit the generalizability of the proposed framework, requiring further investigation to adapt to diverse application scenarios.
Limited Dataset Scope
The study focuses on a limited set of datasets, including English, Spanish, and code datasets, which may not be representative of the broader scope of language models and applications.
Expert Commentary
This article presents a significant contribution to the field of language model compression, offering a novel approach to model optimization through knowledge distillation and guided chain-of-thought reinforcement learning. The authors' findings demonstrate the efficacy of their method across various language models, including English, Spanish, and code datasets. However, the study's limitations, including the reliance on carefully tuned hyperparameters, warrant further investigation to adapt to diverse application scenarios. The proposed framework has significant practical implications for efficient model deployment in resource-constrained settings, making it an essential consideration for researchers and practitioners in the field.
Recommendations
- ✓ Future studies should investigate the generalizability of the proposed framework across diverse language models and datasets.
- ✓ Researchers should explore the application of the proposed framework in various domains, including natural language processing, machine translation, and text summarization.