## Line Graph: Gradient Norm vs Training Steps ### Overview The graph displays four distinct training runs comparing gradient norm magnitudes across 100,000 training steps. Each line represents different combinations of normalization techniques and gradient clipping strategies, with the y-axis showing gradient norm values and the x-axis showing training progression. ### Components/Axes - **X-axis**: Training Step (0K to 100K in 20K increments) - **Y-axis**: Gradient Norm (0 to 30) - **Legend**: - Blue circles: Pre-Norm | Gradient Clip=10 | Run 1 - Orange triangles: Pre-Norm | Gradient Clip=1 | Run 2 - Green triangles: Post-Norm | DeepNorm | Gradient Clip=1 - Pink diamonds: Pre-Norm | Attn. Relax | Gradient Clip=1 ### Detailed Analysis 1. **Blue Line (Gradient Clip=10)**: - Starts at ~10 at 0K - Peaks at **30** at 20K (highest point in graph) - Drops below 10 by 40K - Ends at ~8 at 100K 2. **Orange Line (Gradient Clip=1)**: - Starts at ~10 at 0K - Peaks at **20** at 20K - Fluctuates between 10-15 after 40K - Ends at ~12 at 100K 3. **Green Line (Post-Norm/DeepNorm)**: - Remains flat at **0-2** throughout - Sharp spike to 3 at 20K - Returns to baseline by 40K 4. **Pink Line (Attn. Relax)**: - Starts at ~10 at 0K - Peaks at **25** at 20K - Declines to ~12 at 40K - Stabilizes between 12-15 after 60K ### Key Observations - **Gradient Clip=10** (blue) shows the most extreme early spike but fastest decay - **Post-Norm/DeepNorm** (green) maintains consistently lowest norms - **Attn. Relax** (pink) demonstrates sustained reduction compared to baseline - All configurations show initial volatility at 20K training steps ### Interpretation The data suggests: 1. **Gradient clipping magnitude** directly impacts early training dynamics - higher clipping (10) causes extreme initial spikes but faster stabilization 2. **Post-Norm/DeepNorm** (green) achieves most stable training through effective gradient suppression 3. **Attention relaxation** (pink) provides moderate but sustained norm reduction compared to standard pre-norm 4. The 20K spike across all runs may indicate a critical training phase where gradient magnitudes peak before optimization stabilizes This pattern demonstrates how different regularization techniques trade off between early training stability and long-term gradient control. The Post-Norm/DeepNorm configuration appears most effective for maintaining consistently low gradient norms throughout training.