diff --git a/README.md b/README.md
index 7b6717f..2084cfa 100644
--- a/README.md
+++ b/README.md
@@ -24,25 +24,38 @@ This repository includes optimized scripts for running FastTD3 on the HoReKa sup
 git clone https://github.com/younggyoseo/FastTD3.git
 cd FastTD3
 
-# Install Python 3.10 locally (HoReKa doesn't provide conda)
+# Install Python 3.10 locally with cross-CPU compatibility
+# IMPORTANT: Use generic x86-64 architecture for compatibility with both Intel and AMD nodes
 mkdir -p $HOME/.local/python-3.10
 cd /tmp
 curl -O https://www.python.org/ftp/python/3.10.14/Python-3.10.14.tgz
 tar -xzf Python-3.10.14.tgz
 cd Python-3.10.14
-./configure --prefix=$HOME/.local/python-3.10 --enable-optimizations --with-ensurepip=install
+
+# Configure without Intel-specific optimizations for AMD EPYC compatibility
+export EXTRA_CFLAGS="-march=x86-64 -mtune=generic"
+./configure --prefix=$HOME/.local/python-3.10 \
+    --with-ensurepip=install \
+    --enable-shared \
+    CFLAGS="$EXTRA_CFLAGS" \
+    CPPFLAGS="$EXTRA_CFLAGS"
 make -j$(nproc)
 make install
 
-# Add to PATH
+# Add to PATH and set library path
 echo 'export PATH="$HOME/.local/python-3.10/bin:$PATH"' >> ~/.bashrc
+echo 'export LD_LIBRARY_PATH="$HOME/.local/python-3.10/lib:$LD_LIBRARY_PATH"' >> ~/.bashrc
 echo 'export PATH="$HOME/.local/python-3.10/bin:$PATH"' >> ~/.zshrc
+echo 'export LD_LIBRARY_PATH="$HOME/.local/python-3.10/lib:$LD_LIBRARY_PATH"' >> ~/.zshrc
 export PATH="$HOME/.local/python-3.10/bin:$PATH"
+export LD_LIBRARY_PATH="$HOME/.local/python-3.10/lib:$LD_LIBRARY_PATH"
 
 # Go back to FastTD3 directory
 cd $HOME/path/to/FastTD3
 
 # Create virtual environment and install dependencies
+# NOTE: If you encounter library errors, ensure LD_LIBRARY_PATH is set correctly
+source ~/.bashrc  # Load PATH and LD_LIBRARY_PATH
 $HOME/.local/python-3.10/bin/python3.10 -m venv .venv
 source .venv/bin/activate
 pip install --upgrade pip
@@ -100,15 +113,17 @@ sbatch run_fasttd3.slurm
 ### Configuration
 
 The setup includes:
+- **Cross-CPU compatible Python 3.10** with generic x86-64 architecture (works on both Intel Xeon and AMD EPYC nodes)
 - **SLURM scripts** (`run_fasttd3.slurm`, `run_fasttd3_full.slurm`) configured for accelerated partition with GPU
 - **Job helpers** (`submit_job.py`, `submit_experiment_batch.py`) for single/batch job submission
 - **Monitoring tool** (`monitor_experiments.py`) for real-time experiment tracking
 - **Test script** (`test_setup.py`) for environment verification
 - **Experiment plan** (`experiment_plan.md`) with current progress and TODO tracking
-- **MuJoCo Playground environment** (`T1JoystickFlatTerrain`) working and tested
+- **MuJoCo Playground environment** (`T1JoystickFlatTerrain`) working and tested on all node types
 - **Automatic GPU detection** and CUDA 12.4 compatibility  
 - **Wandb logging** with online mode by default
 - **Paper-accurate hyperparameters** for systematic replication
+- **LD_LIBRARY_PATH configuration** for shared Python libraries
 
 ### Wandb Integration