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+# Gradient Clipping Experiment
+
+## Objective
+Demonstrate how gradient clipping stabilizes training by preventing sudden large weight updates caused by rare, high-loss data points.
+
+## Task Breakdown
+
+- [ ] Step 1: Implement simple PyTorch model (Embedding + Linear)
+- [ ] Step 2: Create imbalanced synthetic dataset (990 'A', 10 'B' targets)
+- [ ] Step 3: Training loop WITHOUT gradient clipping - record metrics
+- [ ] Step 4: Training loop WITH gradient clipping (threshold=1.0) - record metrics
+- [ ] Step 5: Generate comparison plots
+- [ ] Step 6: Write summary report with findings
+
+## Key Metrics to Track
+1. Training loss per step
+2. L2 norm of gradients (before clipping)
+3. L2 norm of model weights
+
+## Expected Outcome
+- Without clipping: Spiky gradient norms when encountering rare 'B' samples
+- With clipping: Bounded gradient norms, more stable training