Update README.md

README.md

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 ## 1. Model Summary
 The **ReLLM-C1** model is a Large Language Model (LLM) specifically fine-tuned to act as a surrogate model for **single objective optimization** in computationally expensive optimization tasks. 
 
-It serves as a core modeling component within the **R2SAEA** (Reinforced Relation Surrogate-Assisted Evolutionary Algorithm) framework. Unlike general-purpose LLMs, ReLLM-C2 is designed to seamlessly integrate with Evolutionary Algorithms (EAs). By leveraging structured prompt templates containing decision variables and objective data, the model can perform zero-shot relationship reasoning to evaluate and classify candidate solutions in multi-objective optimization scenarios.
+It serves as a core modeling component within the **R2SAEA** (Reinforced Relation Surrogate-Assisted Evolutionary Algorithm) framework. Unlike general-purpose LLMs, ReLLM-C1 is designed to seamlessly integrate with Evolutionary Algorithms (EAs). By leveraging structured prompt templates containing decision variables and objective data, the model can perform zero-shot relationship reasoning to evaluate and classify candidate solutions in multi-objective optimization scenarios.
 
 ## 2. Intended Use
 *   **Primary Application:** Relational-based surrogate modeling in multi-objective Evolutionary Algorithms.
@@ -16,7 +16,7 @@ It serves as a core modeling component within the **R2SAEA** (Reinforced Relatio
 This model bridges the gap between **Large Language Models (LLMs)** and **Evolutionary Algorithms (EAs)**, addressing a critical bottleneck in the field of Surrogate-Assisted Evolutionary Algorithms (SAEAs):
 *   **The Problem with Traditional SAEAs:** Conventional machine learning surrogate models (such as Gaussian Processes or Random Forests) require being retrained from scratch at every single generation using new evaluated data, which introduces massive computational overhead.
 *   **Our Methodology:** Through the R2SAEA framework, we transform the relationship reasoning problem in optimization tasks into a **Reinforcement Learning (RL)** problem. 
-*   **Training Alignment:** ReLLM-C2 is trained using the **Group Relative Policy Optimization (GRPO)** algorithm. This aligns the LLM's reasoning capabilities directly with multi-objective optimization goals, granting it the ability to perform zero-shot classification across a wide range of unseen tasks. This eliminates the need for generation-by-generation retraining while significantly reducing the computational burden associated with using general-purpose LLMs.
+*   **Training Alignment:** ReLLM-C1 is trained using the **Group Relative Policy Optimization (GRPO)** algorithm. This aligns the LLM's reasoning capabilities directly with multi-objective optimization goals, granting it the ability to perform zero-shot classification across a wide range of unseen tasks. This eliminates the need for generation-by-generation retraining while significantly reducing the computational burden associated with using general-purpose LLMs.
 
 ## 4. GitHub Repository
 To utilize ReLLM-C1 effectively, it should be deployed alongside the **R2SAEA framework**, which handles prompt structuring and the evolutionary loop. The framework provides implementations in both **Python** (via pymoo) and **MATLAB** (via PlatEMO).