## 5 Conclusions  
This study extensively explores the significance of parameter selection in the model fusion process, with a focus on developing high-performance fusion models. We propose an innovative approach, RL-Fusion, which significantly improves model performance by \begin{table}
\begin{tabular}{l l l l l} \hline  & **Time** & **Hardware Resource** & **MMLU** & **C-Eval** \\ \hline FineTune & 60 min & RTX4090 24G & **68.5\%** & **79.2\%** \\ \hline RL–Fusion & **15 min** & **cpu** & **70.8\%** & **78.6\%** \\ \hline \end{tabular} Bolded content is the experimental results of this paper.  
\end{table} 
Table 6: Overall results of the comparison of RL–Fusion LLM and fine-tuning methods. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **MMLU** & **C-Eval** \\ \hline RL–Fusion & **70.8\%** & **78.6\%** \\ \hline Slerp [30] & 68.7\% & 74.1\% \\ \hline TIES [13] & 66.9\% & 76.2\% \\ \hline Llama 3.1-8B [33] & 73.0\% & 81.2\% \\ \hline LLaMA 2-7B [5] & 45.3\% & 75.2\% \\ \hline Mixtral 8x7B [34] & 70.6\% & 74.7\% \\ \hline Qwen 7B [35] & 56.7\% & 59.6\% \\ \hline GPT-3 6.7B [19] & 43.2\% & 54.4\% \\ \hline RWKV v5 Eagle 7B [36] & 31\% & 46.7\% \\ \hline Falcon 7B [37] & 28.0\% & 39.2\% \\ \hline \end{tabular} Bolded content is the experimental results of this paper.  
\end{table} 
Table 7: Overall results of the comparison of RL–Fusion LLM and Other LLMs. leveraging reinforcement learning (RL) algorithms in the fusion process, combined with real-time model evaluation feedback. By dynamically adjusting the fusion parameters and evaluating the dataset, RL-Fusion effectively navigates the parameter space to discover optimal combinations. Notably, RL-Fusion enhances model performance in tasks such as Named Entity Recognition (NER), significantly improving accuracy while preserving the base model's original functionality. Through a series of well-designed experiments, we validate the significant advantages of the RL-Fusion approach over a single large language model (LLM) and established benchmarks. The experimental results demonstrate that RL-Fusion outperforms traditional methods across several key metrics, owing to its intelligent optimization of parameter selection. Specifically, we employed multiple LLMs with the same structure as the base model in our experiments to evaluate RL-Fusion's performance under different model configurations. This setup enables a comprehensive evaluation of RL-Fusion's applicability and effectiveness across various model fusion scenarios. However, deploying RL-Fusion in real-world applications presents several challenges. First, the computational overhead of reinforcement learning during the fusion process, particularly for high-dimensional parameter spaces, may limit its scalability for extremely large models. Second, the dynamic evaluation mechanism, while effective, requires access to high-quality, domain-specific evaluation datasets, which may not always be readily available. Third, the real-time feedback loop, though innovative, introduces additional latency, which could be a bottleneck in time-sensitive applications. Finally, the generalizability of RL-Fusion across diverse tasks and domains needs further investigation, as its performance may vary depending on the complexity and specificity of the target task. In summary, RL-Fusion leverages the adaptive learning capabilities of RL to offer a novel and powerful solution in model fusion. Our research not only advances model fusion techniques but also offers new insights into more efficient parameter optimization and improved model performance across a range of AI applications. We anticipate that RL-Fusion will be widely adopted in future research and practice to address more complex model fusion challenges and contribute to the advancement of AI technology. Future work will focus on addressing these deployment challenges to make RL-Fusion more accessible and scalable for real-world use cases. Conceptualization, J.L. ; methodology, Z.W., J.L. and C.Y. ; software, Z.W. and X.L. ; validation, Z.W. ; formal analysis, Y.L.