Update README.md

README.md

@@ -42,7 +42,7 @@ LightBulb provides six primary functionalities, each accessible via the `main_me
 Trains an autonomous web search agent that navigates the web, gathers relevant content, and learns to summarize and generate responses based on user queries.
 ## Overview of the AutonomousWebAgent
 
-The AutonomousWebAgent is a sophisticated, multi-component search and retrieval agent designed to navigate the web, gather relevant content, and perform summarization and generation based on user queries. This agent integrates reinforcement learning (RL), Monte Carlo Tree Search (MCTS), a Retrieval Augmented Generation (RAG) Summarizer, and a Hierarchical Reinforcement Learning (HRL) architecture to select, execute, and optimize its actions based on past experiences.
+The AutonomousWebAgent is a multi-component search and retrieval agent designed to navigate the web, gather relevant content, and perform summarization and generation based on user queries. This agent integrates reinforcement learning (RL), Monte Carlo Tree Search (MCTS), a Retrieval Augmented Generation (RAG) Summarizer, and a Hierarchical Reinforcement Learning (HRL) architecture to select, execute, and optimize its actions based on past experiences.
 
 ### Key Components
 
@@ -118,10 +118,10 @@ python main_menu.py --task test_agent
   python main_menu.py --task test_agent --query "Your query here"
   ```
 
-### 3. Train a Language Model
+### 3. Train Language Model
 
 **Description:**  
-Trains a Language Model (LLM) and World Model using datasets from Hugging Face, enabling the model to handle complex reasoning and long sequences.
+Trains a Language Model and World Model using datasets from Hugging Face, enabling the model to handle complex reasoning and long sequences.
 
 ### Training Procedure
 - **Data Loading**: The data is tokenized and prepared with attention to padding and truncation. Text data is grouped into sequences of fixed length for efficient training.
@@ -140,7 +140,7 @@ python main_menu.py --task train_llm_world --model_name gpt2 --dataset_name wiki
 - `--batch_size`: Number of samples per batch.
 - `--max_length`: Maximum sequence length.
 
-### 4. Inference Using Language Model with Multi-Token Prediction, Beam Search, and MCTS
+### 4. Inference Using Language Model
 
 **Description:**  
 Generates responses using the trained language model, leveraging multi-token prediction, beam search, and MCTS for enhanced coherence and strategic reasoning.
@@ -155,7 +155,7 @@ python main_menu.py --task inference_llm --query "Your query here"
 2. **Beam Search:** Maintains multiple candidate sequences to ensure diverse and high-quality outputs.
 3. **MCTS Integration:** Uses MCTS to evaluate and select the most promising token sequences based on policy and value estimates.
 
-### 5. Train a Language World Model
+### 5. Train World Model
 
 **Description:**  
 Develops a comprehensive World Model that encapsulates state representations, dynamics, and prediction networks to simulate and predict state transitions within the Tree of Thought framework.
@@ -268,7 +268,6 @@ python main_menu.py --task inference_world_model --query "Your query here"
 Executes inference using the World Model integrated with ToT and multi-token beam search for highly coherent and contextually rich outputs.
 
 
-
 **Usage:**
 ```bash
 python main_menu.py --task advanced_inference --query "Your complex query here"
@@ -528,9 +527,9 @@ graph TD
   - `argparse`
   - `huggingface_hub`
 
-## Usage Examples
 
-### Training the Language Model and World Model
+
+### Training the World Model
 
 ```bash
 python main_menu.py --task train_llm_world --model_name gpt2 --dataset_name wikitext --num_epochs 5 --batch_size 8 --max_length 256
@@ -542,19 +541,19 @@ python main_menu.py --task train_llm_world --model_name gpt2 --dataset_name wiki
 python main_menu.py --task train_agent
 ```
 
-### Testing the Web Search Agent in Interactive Mode
+### Use the Web Search Agent in Interactive Mode
 
 ```bash
 python main_menu.py --task test_agent
 ```
 
-### Testing the Web Search Agent with a Single Query
+### Use the Web Search Agent with a Single Query
 
 ```bash
 python main_menu.py --task test_agent --query "What are the impacts of renewable energy on global sustainability?"
 ```
 
-### Advanced Inference with World Model and Tree of Thought
+### Inference with World Model and Tree of Thought
 
 ```bash
 python main_menu.py --task advanced_inference --query "Analyze the economic effects of artificial intelligence in the next decade."