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Test Optimization Summary
Changes Made
1. Added tqdm Progress Bars β
Before: No progress indicators - tests appeared frozen After: Progress bars show:
- Training iterations progress
- Task processing status
- Time elapsed
Example output:
Testing learning capability...
Generating eval set... Done
Evaluating initial accuracy... 0.250
Training on 15 tasks:
Progress: 100%|ββββββββ| 15/15 [00:02<00:00]
Evaluating final accuracy... 0.400
β
Learning verified (improvement: +0.150)
2. Optimized Test Iterations
- Reduced training iterations: 30 β 15, 40 β 20
- Smaller eval sets: 10 β 5 tasks
- Faster forgetting: Shorter time advances
3. Better Progress Messages
- Clear status messages for each step
- Shows what's happening (generating, evaluating, training)
- Total time at the end
Why Tests Are Slow
Main cause: DistilBERT model loading
- Downloads ~260MB model (first time)
- Loads model weights into memory
- Can take 10-30 seconds per test
This is normal - not your laptop's fault! Neural networks are just large.
Performance Tips
First run is slowest (downloads model)
- Subsequent runs use cached model (faster)
Install tqdm for progress bars:
pip install tqdmGPU would be faster but requires CUDA setup
Progress bars help even if slow - you see what's happening!
Test Output Example
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RUNNING STUDENT AGENT TESTS
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Testing student initialization... β
Student model initialized
Testing answer prediction... β
Student can answer tasks
Testing learning capability...
Generating eval set... Done
Evaluating initial accuracy... 0.250
Training on 15 tasks:
Progress: 100%|ββββββββ| 15/15 [00:02<00:00]
Evaluating final accuracy... 0.400
β
Learning verified (improvement: +0.150)
...
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π All tests passed! (Total time: 45.32s)
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The progress bars make it clear what's happening even if it takes time!