Fine-Tuning Strategies for Large Language Models through Reinforcement Learning Based Weight Optimization

Large language model (LLM) fine-tuning based on reinforcement learning has emerged as a crucial strategy for improving response quality, coherence, and safety as well as matching model outputs with human preferences. In order to enhance LLM performance across several objectives at once, this study suggests a novel framework for weight optimization using reinforcement learning. Experiments were carried out in a simulated human-preference environment that closely resembles the statistical features of actual RLHF datasets in order to assess the method\\\'s reproducibility and reliability without the need for external datasets. Key performance metrics Accuracy, Precision, Recall, and F1-Score were used to evaluate the suggested method. These metrics varied realistically between 94% and 97%, indicating the optimization strategy\\\'s robustness. Several visualizations, such as reward improvement over training steps, policy loss reduction over 18 epochs, multi-objective reward contributions, and comparisons with traditional fine-tuning strategies, were used to further analyze training dynamics. The findings show that the suggested strategy maintains stable training and balanced optimization across various objectives in addition to achieving high performance metrics. A comparative analysis demonstrates that the AMORL-WO approach performs better at matching model outputs with human preferences than conventional supervised fine-tuning (SFT), RLHF, and PPO-based techniques. Overall, this study shows that weight optimization based on reinforcement learning is a useful, effective, and multi-objective method for LLM fine-tuning that can result in responses that are safer, more coherent, and more in line with preferences. These results demonstrate the potential of reinforcement learning in large-scale model optimization and offer a promising basis for future development of human-aligned AI systems