Curriculum Learning Issues
Curriculum learning issues arise from poorly designed difficulty progression that hurts rather than helps training.
Curriculum learning issues arise from poorly designed difficulty progression that hurts rather than helps training.
What this failure is
Curriculum Learning Issues is a Training Stability failure seen during ML training runs. Curriculum learning issues arise from poorly designed difficulty progression that hurts rather than helps training. Common tags: Curriculum, Self Paced, Difficulty, Training Stability.
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Why it happens (the mechanism)
Difficulty measure doesn't reflect model learning. Curriculum too easy for too long. Curriculum jumps to hard examples too fast. Easy examples over-trained, hard examples under-trained. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- Curriculum learning doesn't improve over baseline
- Model performs worse with curriculum
- Curriculum progression is too aggressive
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Final accuracy with curriculum is lower than baseline | Difficulty measure doesn't reflect model learning |
| Easy examples don't transfer to hard | Curriculum too easy for too long |
| Difficulty measure doesn't correlate with model needs | Curriculum jumps to hard examples too fast |
Which systems are affected
- Training with curriculum learning
- Self-paced learning
- Hard example mining
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: Final accuracy with curriculum is lower than baseline
- ✓Verified signal present: Easy examples don't transfer to hard
- ✓Verified signal present: Difficulty measure doesn't correlate with model needs
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
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Root cause
- Difficulty measure doesn't reflect model learning
- Curriculum too easy for too long
- Curriculum jumps to hard examples too fast
- Easy examples over-trained, hard examples under-trained
The fix and how to prevent it
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