docs: Update TRL README with GRPO example details and usage instructions (#76)

examples/trl/README.md

@@ -1,5 +1,32 @@
-1. Install the requirements in the txt file
+# TRL Examples
 
-```
+This directory contains examples using the [TRL (Transformer Reinforcement Learning) library](https://github.com/huggingface/trl) to fine-tune language models with reinforcement learning techniques.
+
+## GRPO Example
+
+The main example demonstrates using GRPO (Group Relative Policy Optimization) to fine-tune a language model on reasoning tasks from reasoning-gym. It includes:
+
+- Custom reward functions for answer accuracy and format compliance
+- Integration with reasoning-gym datasets
+- Configurable training parameters via YAML config
+- Wandb logging and model checkpointing
+- Evaluation on held-out test sets
+
+## Setup
+
+1. Install the required dependencies:
+
+```bash
 pip install -r requirements.txt
 ```
+
+## Usage
+
+1. Configure the training parameters in `config/grpo.yaml`
+2. Run the training script:
+
+```bash
+python main_grpo_reward.py
+```
+
+The model will be trained using GRPO with the specified reasoning-gym dataset and evaluation metrics will be logged to Weights & Biases.