EVALUATION_STATUS.md

Comprehensive Nguyen Evaluation - Current Status

Last Updated: 2026-02-11 14:45 UTC

✅ Status: RUNNING SUCCESSFULLY

All 4 critical bugs have been fixed and verified in production. The evaluation is running smoothly.

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📊 Current Progress

Evaluation Started: 2026-02-11 14:36:40 UTC Instance: i-051cad4bd51af8746 (g5.2xlarge, IP: 3.81.72.206) Process PID: 7776 (main) + 7778 (training)

Configuration:

• Models: 4 (base_prefix, medium_prefix, large_prefix, base_infix)
• Benchmarks: 12 (Nguyen 1-12)
• Algorithms: 2 (PPO, GRPO)
• Total Experiments: 96
• Epochs per experiment: 20

Progress: 1/96 experiments completed (1.0%) Time per experiment: ~5 minutes Estimated completion: 22:30-23:00 UTC (2026-02-11) Estimated duration: 8 hours total

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✅ Verification Results (First Experiment)

Experiment: base_prefix + nguyen_1 + PPO Status: ✅ Completed successfully at 14:41 UTC

Results:

{
  "best_expression": "- C exp * C x_1",
  "best_r2": 0.8638,
  "best_epoch": 6,
  "total_epochs": 20,
  "final_valid_rate": 15.6%
}

Expression Quality ✅:

• Clean prefix notation (no JSON artifacts)
• Proper stopping at expression boundaries
• Valid mathematical expressions
• R² improvement through RL (epoch 0: -1.0 → epoch 6: 0.8638)

Sample valid expressions:

1. - * * -1 C x_1 sin - * C x_1 C | R²=0.2443
2. - C ** - * C x_1 C C | R²=-0.0686
3. - C exp * C x_1 | R²=0.8638 ← Best

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🔧 All Bugs Fixed

Bug #1: GPU Not Loaded ✅

• Before: CPU-only, 30+ min timeouts
• After: GPU active, 40% utilization, 5 min/experiment

Bug #2: Missing Stopping Criteria ✅

• Before: Infinite generation with JSON pollution
• After: Clean stopping at newlines for prefix notation

Bug #3: Dirty Expression Extraction ✅

• Before: JSON artifacts in expressions ("}", "cons", etc.)
• After: Clean extraction with all markers removed

Bug #4: LoRA Gradients Not Enabled ✅

• Before: RuntimeError, no gradient flow, 100% failure
• After: Gradients working, R² improving, training functional

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💻 System Status

GPU: NVIDIA A10G (g5.2xlarge)

• Utilization: 40%
• VRAM Used: 1,176 MiB / 23 GB
• Temperature: 40°C

Storage:

• Results directory: ~/seriguela/evaluation_results/20260211_143640/
• Log file: ~/seriguela/evaluation_complete.log

Files Being Generated:

• full_history.json - All epochs, all expressions, all R² scores (133K per experiment)
• summary.json - Best expression and results (192B per experiment)
• checkpoint-{4,9,14,19}/ - Model checkpoints every 5 epochs
• raw_results.json - Aggregated results across all experiments

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📅 Monitoring Schedule

Current time: 14:45 UTC Next checks:

• ✅ 14:45 UTC - Verification complete (DONE)
• 🔄 16:00 UTC - 20 experiments (20%)
• 🔄 18:00 UTC - 40 experiments (42%)
• 🔄 20:00 UTC - 60 experiments (63%)
• 🔄 22:00 UTC - 80 experiments (83%)
• 🎯 23:00 UTC - Expected completion (96 experiments)

How to check progress:

bash check_evaluation_progress.sh

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📦 What Happens When Complete

1. Automatic Data Collection

The evaluation will automatically save:

• Full history files (96 × 133K = ~12.8 MB): All expressions, R² scores, epochs
• Summary files (96 × 192B = ~18 KB): Best results per experiment
• Checkpoints (96 × 4 checkpoints): Model states at epochs 4, 9, 14, 19
• Aggregated results (raw_results.json): Complete dataset for analysis

2. Manual Steps Required

When the evaluation completes (~23:00 UTC), you must:

A. Download Results

# From Windows local machine
scp -i C:/Users/madeinweb/chave-gpu.pem -r \
  ubuntu@3.81.72.206:~/seriguela/evaluation_results/20260211_143640 \
  ./evaluation_results_aws/

# Also download the log
scp -i C:/Users/madeinweb/chave-gpu.pem \
  ubuntu@3.81.72.206:~/seriguela/evaluation_complete.log \
  ./evaluation_results_aws/

B. Verify All Results Downloaded

# Check we have all 96 experiments
find evaluation_results_aws/20260211_143640 -name "summary.json" | wc -l
# Should output: 96

C. Run Academic Analysis

python scripts/analyze_evaluation_results.py \
  --input_dir ./evaluation_results_aws/20260211_143640 \
  --output_dir ./analysis_results

This will generate:

• LaTeX tables comparing all models
• R² heatmaps across benchmarks
• Expression complexity analysis
• Statistical significance tests
• Academic-quality visualizations

D. Commit Everything to GitHub

git add evaluation_results_aws/ analysis_results/
git commit -m "Complete: Comprehensive Nguyen evaluation results

96 experiments completed:
- 4 models × 12 benchmarks × 2 algorithms
- All expressions collected with R² scores
- Full training history saved
- Academic analysis generated

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>"

git push origin experiment/ppo-symbolic-regression

E. CRITICAL: Stop AWS Instance

# Stop the instance to avoid charges
aws ec2 stop-instances --instance-ids i-051cad4bd51af8746

# Verify it stopped
aws ec2 describe-instances \
  --instance-ids i-051cad4bd51af8746 \
  --query "Reservations[0].Instances[0].State.Name"
# Should output: "stopping" or "stopped"

Cost so far: ~$1.21/hour × 8 hours = ~$9.68 USD WARNING: If you forget to stop, charges continue at $1.21/hour!

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📊 Expected Results

Based on first experiment, we expect:

Overall Statistics:

• Total expressions generated: 96 × 20 epochs × 32 samples = 61,440 expressions
• Valid expression rate: ~15-20%
• Total valid expressions: ~9,200-12,300
• Best R² range: 0.5 - 1.0 (depending on benchmark complexity)

Model Comparisons:

• Prefix models (3 sizes) vs Infix model (1 size)
• PPO vs GRPO algorithm effectiveness
• Model size impact: Base (124M) vs Medium (355M) vs Large (774M)
• Benchmark difficulty: Easy (Nguyen 1-3) vs Hard (Nguyen 10-12)

Key Research Questions:

1. Do larger models generate more complex expressions?
2. Which RL algorithm (PPO vs GRPO) works better?
3. Does prefix notation outperform infix?
4. How does performance vary across Nguyen benchmarks?

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📁 File Locations

AWS Instance:

• Results: ubuntu@3.81.72.206:~/seriguela/evaluation_results/20260211_143640/
• Log: ubuntu@3.81.72.206:~/seriguela/evaluation_complete.log

Local (after download):

• Results: ./evaluation_results_aws/20260211_143640/
• Analysis: ./analysis_results/
• Monitoring script: ./check_evaluation_progress.sh
• This status file: ./EVALUATION_STATUS.md

GitHub:

• Branch: experiment/ppo-symbolic-regression
• Latest commits:
  • 99e7509 - Verify: All 4 bugs fixed, first experiment successful
  • 8643a00 - Fix: Enable gradients with enable_input_require_grads()
  • 9266d13 - Fix: Clean JSON artifacts from extraction
  • 2ba6726 - Fix: Add prefix stopping criteria
  • 21ce35b - Start comprehensive Nguyen evaluation on AWS

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🆘 Troubleshooting

If evaluation seems stuck:

# Check process is still running
ssh -i C:/Users/madeinweb/chave-gpu.pem ubuntu@3.81.72.206 'ps aux | grep run_comprehensive'

# Check GPU is active
ssh -i C:/Users/madeinweb/chave-gpu.pem ubuntu@3.81.72.206 'nvidia-smi'

# View latest logs
ssh -i C:/Users/madeinweb/chave-gpu.pem ubuntu@3.81.72.206 'tail -50 ~/seriguela/evaluation_complete.log'

If you need to restart:

# SSH into instance
ssh -i C:/Users/madeinweb/chave-gpu.pem ubuntu@3.81.72.206

# Kill the process
pkill -f run_comprehensive_evaluation

# Restart from where it left off (if implemented) OR start fresh
cd ~/seriguela
python scripts/run_comprehensive_evaluation.py \
  --output_dir ./evaluation_results \
  --epochs 20 \
  --algorithms ppo grpo \
  > evaluation_complete.log 2>&1 &

If running out of disk space:

# Check disk usage
ssh -i C:/Users/madeinweb/chave-gpu.pem ubuntu@3.81.72.206 'df -h'

# If needed, delete checkpoints (they're large but not critical)
ssh -i C:/Users/madeinweb/chave-gpu.pem ubuntu@3.81.72.206 \
  'find ~/seriguela/evaluation_results -name "checkpoint-*" -type d -exec rm -rf {} +'

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✅ Success Criteria

The evaluation is considered successful when:

• ✅ All 96 experiments complete without errors
• ✅ All results downloaded to local machine
• ✅ Academic analysis generates LaTeX tables and plots
• ✅ Everything committed to GitHub
• ✅ AWS instance stopped

Current status: 1/5 criteria met (evaluation running)

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For questions or issues: Check EVALUATION_DEBUGGING_SUMMARY.md for detailed bug history and fixes.