## 4 Experiments and Results
### Experiments
#### 4.4.1 Ablation Experiment  
To validate the effectiveness of the proposed RL-Fusion approach, we conducted a series of ablation experiments to evaluate the contribution of each component to the overall performance by progressively disassembling each part of RL-Fusion. The ablation experiments used a reinforcement learning (RL)-based selection strategy. In addition, two model fusion strategies were implemented: one using only RL for parameter selection (at which point the evaluation dataset was in the top 5% of the Yayi dataset), and the other combining reinforcement learning with dynamic model evaluation feedback (dynamically adjusting the number of evaluation datasets based on NER evaluation results). To further evaluate the effectiveness of these strategies, we fine-tuned the base model on the Chinese Named Entity Recognition (NER) dataset and evaluated its performance on the MMLU and C-Eval datasets (all sub-tasks; results are shown in Figures 4 and 5), as well as on the Yayi NER dataset (results are shown in Figure 6). Figure 4: Results of ablation experiments on MMLU. Figure 5: Results of ablation experiments on C-eval. In order to validate the effectiveness of the proposed RL-Fusion approach, we conducted a series of ablation experiments to evaluate the contribution of each component to the overall performance by progressively decomposing the parts of RL-Fusion. The ablation experiments used a reinforcement learning (RL)-based selection strategy. In addition, two model fusion strategies were implemented: one using only RL for parameter selection with a fixed evaluation dataset, and the other combining reinforcement learning with dynamic model evaluation feedback to dynamically adjust the evaluation dataset through evaluation feedback. To further evaluate the effect of RL-Fusion on fine-tuning the model, we fine-tuned the pre-fusion base model, llama-3-chinese-8b-instruct-v3, on the Chinese Named Entity Recognition (NER) dataset and performed model fusion with RL-Fusion on both the MMLU and C-Eval datasets (across all subtasks). As an additional baseline, we also compared the performance of RL-Fusion against a randomly parameterized fusion model, where the model parameters were fused without any optimization strategy, serving as a comparison to assess the impact of RL-based adjustments. The results are shown in Figures 4 and 5, and the Yayi NER dataset (results are shown in Figure 6) to evaluate its performance. The experimental results, presented in Table 2, clearly demonstrate that the RL-Fusion method outperforms other single-parameter selection strategies across all evaluation metrics. Furthermore, after fine-tuning the base model on the Chinese NER data, applying the RL-Fusion fusion strategy led to significant performance improvements, particularly on the Yayi (calculated by Equation (5)) and C-Eval (calculated by Equation (15)) datasets. The relatively lower performance on the MMLU (calculated by Equation (15)) dataset can be attributed to the fine-tuning enhancing the model's knowledge of NER but also causing the fine-tuned model to underperform on more generalized data. These results underscore the superiority of the RL-Fusion method in optimizing model performance for Named Entity Recognition (NER) tasks. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **MMLU** & **C-Eval** & **Yayi(NER)** \\ \hline Baseline & 68.2\% & 76.7\% & 2.1 \\ \hline RL & 70.1\% & 77.9\% & 2.4 \\ \hline RL–Fusion & **70.8\%** & **78.6\%** & **2.5** \\ \hline RL–Fusion+FineTune & 69.5\% & **80.2\%** & **3.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the ablation experiments with the RL–Fusion method. Figure 6: Results of ablation experiments on Yayi. Based on the experimental results, RL-Fusion demonstrates notable performance improvements across the MMLU, C-Eval, and Yayi (NER) datasets. On the MMLU dataset, RL-Fusion outperforms the baseline by 2.6%, increasing from 68.2% to 70.8%, and also surpasses RL by 0.7%, rising from 70.1% to 70.8%.