## Line Graph: Gradient Norms During Training ### Overview The line graph displays the gradient norms over training steps for four different normalization techniques: Pre-Norm, Post-Norm, DeepNorm, and Attn. Relax. The gradients are clipped at a maximum value of 10 for Pre-Norm and 1 for the other techniques. ### Components/Axes - **X-axis (Training Step)**: Represents the number of training steps, ranging from 0 to 100,000. - **Y-axis (Gradient Norm)**: Represents the gradient norm values, ranging from 0 to 30. - **Legend**: Contains four lines, each representing a different normalization technique. - **Pre-Norm | Gradient Clip=10 | Run 1**: Blue line with circular markers. - **Pre-Norm | Gradient Clip=1 | Run 2**: Orange line with circular markers. - **Post-Norm | DeepNorm | Gradient Clip=1**: Green line with square markers. - **Pre-Norm | Attn. Relax | Gradient Clip=1**: Pink line with square markers. ### Detailed Analysis - **Pre-Norm Techniques**: Both Pre-Norm techniques with Gradient Clip=10 and Gradient Clip=1 show a similar trend, with a sharp increase in gradient norm around the 20,000 training step mark, followed by a gradual decrease. - **Post-Norm Technique**: The Post-Norm technique with DeepNorm and Gradient Clip=1 shows a more stable gradient norm, with a slight increase around the 20,000 training step mark and a gradual decrease thereafter. - **Attn. Relax Technique**: The Attn. Relax technique with Gradient Clip=1 shows a similar trend to Pre-Norm techniques, with a sharp increase around the 20,000 training step mark, followed by a gradual decrease. ### Interpretation The data suggests that the normalization techniques have different effects on the gradient norms during training. The Pre-Norm techniques with Gradient Clip=10 and Gradient Clip=1 show a similar trend, with a sharp increase in gradient norm around the 20,000 training step mark, followed by a gradual decrease. This could indicate that the normalization techniques are not significantly affecting the gradient norms during the later stages of training. The Post-Norm technique with DeepNorm and Gradient Clip=1 shows a more stable gradient norm, with a slight increase around the 20,000 training step mark and a gradual decrease thereafter. This could indicate that the normalization techniques are having a more significant effect on the gradient norms during the later stages of training. The Attn. Relax technique with Gradient Clip=1 shows a similar trend to Pre-Norm techniques, with a sharp increase around the 20,000 training step mark, followed by a gradual decrease. This could indicate that the normalization techniques are not significantly affecting the gradient norms during the later stages of training. Overall, the data suggests that the normalization techniques have different effects on the gradient norms during training, with the Post-Norm technique with DeepNorm and Gradient Clip=1 showing a more stable gradient norm.

## 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.