Training Curve Analysis

Analyze existing training logs to understand what happened.

Step 1: Load Logs

Support CSV, JSON, TensorBoard, W&B, or pasted values

Step 2: Plot Overview

Loss curves (train/val), learning rate, gradient norm in 2x2 grid

Step 3: Detect Patterns

• •Healthy training: steady decrease, train/val tracking, stable gradients
• •Loss plateau: detect onset, duration, percentage of training
• •Overfitting: detect onset, final gap
• •Instability: coefficient of variation, severity
• •Divergence: detect onset, preceding signals

Step 4: Statistical Summary

Overview stats, dynamics assessment, key metrics table, recommendations

Step 5: Compare Runs (if multiple)

Overlay plots, comparison table with final/best loss and timing