docs/PART1_MASTER_CHECKLIST.md

MASTER CHECKLIST: WHAT NEEDS TO HAPPEN FOR PART 1

Files Already Prepared (✓ Done)

File	Purpose	Status
PART1_QUICK_SUMMARY.md	1-page reference guide for venue	✓ READY
PART1_DEVELOPMENT_TRAINING_CHECKLIST.md	Detailed step-by-step instructions	✓ READY
generate_curves.py	Curve generation after training	✓ READY
BLOG_POST_TEMPLATE.md	Storytelling framework	✓ READY
training/train.py	Training script	✓ READY
training/config.yaml	Optimized config (1500 episodes)	✓ READY
training/warmup_traces.jsonl	SFT warmup data (20 examples)	✓ READY
permanence/env.py	Core environment	✓ READY

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PART 1: DEVELOPMENT & TRAINING BREAKDOWN

What Happens in PART 1

At venue: 11:30 AM - 8:00 PM (8.5 hours)

PART 1 is about generating evidence that your environment actually teaches agents something.

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WHAT YOU NEED TO DO (Concrete Tasks)

PRE-VENUE (Before you leave today)

Task 1.1: Verify repo is in good state

cd c:\Users\Hp\OneDrive\Desktop\meta
git status  # Should show nothing uncommitted
git log -1  # Last commit: "Add OpenEnv deployment files..."

Expected: No uncommitted changes, repo clean

Task 1.2: Verify dependencies are specified

cat pyproject.toml | grep -A 10 dependencies

Expected: Lists torch, transformers, trl, unsloth, datasets, peft

Task 1.3: Verify training config is correct

cat training/config.yaml

Expected: total_episodes: 1500, group_size: 8, load_in_4bit: true

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AT VENUE: PHASE 1 (11:30 AM - 12:00 PM) — GPU Setup

Task 2.1: Get GPU access

• Find venue staff
• Get SSH credentials or Colab link
• CRITICAL: Confirm GPU type (A100, RTX 4090, H100, etc.)
• If NO GPU: Escalate immediately to L2 mentor

Task 2.2: Verify CUDA works

python -c "import torch; print(torch.cuda.get_device_name(0)); print(f'{torch.cuda.get_device_properties(0).total_memory / 1e9:.0f}GB')"

Expected: Should print GPU name and memory (e.g., "A100" and "40GB")

Task 2.3: Clone repo and install dependencies

git clone https://github.com/chanikkyasaai/permanence
cd permanence
pip install -e .
pip install torch transformers trl unsloth datasets peft

Expected: No errors, all packages install successfully

Task 2.4: Verify environment works

python -c "from permanence.env import PermanenceEnv; print('✓ OK')"

Expected: Prints "✓ OK"

By 12:00 PM: You should have GPU ready, repo cloned, dependencies installed, environment verified.

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AT VENUE: PHASE 2 (12:00 PM - 7:30 PM) — Training Execution

Task 3.1: START TRAINING (single command)

python -m training.train --config training/config.yaml

That's it. Press Enter. Training runs for 7 hours unattended.

What happens next:

• Minutes 0-1: Model loading
• Minutes 1-3: Data loading
• Minutes 3-420: Training (1,500 episodes × ~0.17 min/episode)
• Every 100 episodes: Progress printed to console
• Output: permanence_output/training_log.json with all metrics

You can relax, walk around, eat, prepare for Part 2. Just don't close the terminal.

Checkpoint: Every 500 episodes, a checkpoint is saved. If it crashes at episode 1400, you can resume.

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AT VENUE: PHASE 3 (7:30 PM - 8:00 PM) — Post-Training Verification

Task 4.1: Generate training curves

python generate_curves.py

Expected: Creates results/training_curves.png (4-panel plot)

Task 4.2: Verify curves look good

• Open results/training_curves.png
• Check Panel 1 (Reward): Should trend upward (from negative to positive)
• Check Panel 2 (Loss): Should trend downward (convergence)
• Check Panel 3 (Catastrophe): Should trend downward (improvement)
• Check Panel 4 (Accuracy): Should trend upward (improvement)

If curves look wrong: Check training_log.json for errors

Task 4.3: Verify model loads

python -c "from transformers import AutoModelForCausalLM; m = AutoModelForCausalLM.from_pretrained('./permanence_output/final_model'); print('✓ Model loads')"

Expected: Prints "✓ Model loads"

Task 4.4: Commit results

git add permanence_output/training_log.json results/training_curves.png results/training_summary.txt
git commit -m "Training complete: 1500 episodes, reward improvement verified"

Expected: Commit succeeds, files tracked

By 8:00 PM: You have training curves, metrics, and proof that the environment works.

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DELIVERABLES AT END OF PART 1

By 8:00 PM, you will have:

permanence_output/
├── training_log.json              ← 1,500 episodes of metrics
├── final_model/                   ← Trained weights
│   └── pytorch_model.bin
└── checkpoint_*

results/
├── training_curves.png            ← ⭐ JUDGES WANT THIS
├── training_summary.txt           ← Numerical metrics
└── training_comparison.md

Git commits with all artifacts tracked

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SUCCESS CRITERIA FOR PART 1

✅ You've completed PART 1 if:

• Training ran for 7 hours without crashing
• permanence_output/training_log.json exists with 1,500 episodes
• results/training_curves.png exists and shows improvement
• Reward curve trending upward
• Catastrophe rate trending downward (from ~43% to <20%)
• Prediction accuracy trending upward (from ~31% to >50%)
• Trained model loads successfully
• All results committed to git

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WHAT COMES AFTER PART 1 (PART 2)

Once PART 1 is complete (8:00 PM), you'll have 9 hours until deadline (5:00 PM next day) to do PART 2:

PART 2 Tasks:

1. Write mini-blog or record <2min video explaining results
2. Update README with storytelling arc + curve + links
3. Push to HuggingFace Space
4. Update GitHub with final links
5. Submit Google Form

(PART 2 checklist will be provided separately once PART 1 is done)

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KEY FACTS

PART 1 is the bottleneck. Everything depends on getting GPU training to work.

Judges explicitly state: "At minimum, loss and reward plots from a real run."

Right now: You have 0/20 on "Training Evidence" criterion. After PART 1: You'll have 7/20.

The difference: Disqualification vs. Contention.

What must happen: Train for 7 hours, generate curves, commit results.

Contingency: If GPU fails, you can still explain the technical architecture to judges. But curves are what wins.

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IMMEDIATE NEXT STEPS

Today (Before Venue):

• Print or bookmark PART1_QUICK_SUMMARY.md (2 pages, reference at venue)
• Review PART1_DEVELOPMENT_TRAINING_CHECKLIST.md (detailed steps)
• Verify training/config.yaml one more time
• Make sure laptop has repo cloned locally (backup copy)

At Venue (11:30 AM):

• Find GPU
• Follow PART1_QUICK_SUMMARY.md steps 1-3
• Start training at 12:00 PM
• Follow post-training steps at 7:30 PM
• Curves ready by 8:00 PM

That's the entire PART 1 plan. Nothing more complicated than that.