Self-Adapting Language Models (SEAL)¶

AI-OS Enhancement: Self-Adapting Language Models

Document Version: 1.0
Created: January 15, 2025
Status: Planning Phase
Owner: AI-OS Development Team
Priority: HIGH (Phase 1), MEDIUM (Phase 2), OPTIONAL (Phase 3)

Note: References to docs/user_guide/* in this planning document are placeholders for future user-facing docs. For current information, use docs/INDEX.md and guides under docs/guide/.

Executive Summary¶

Goal: Integrate SEAL (Self-Adapting Language Models) framework into AI-OS to enable autonomous expert training with synthetic data generation and reinforcement learning optimization.

Expected Benefits: - Phase 1: 10-20% improvement in expert quality - Phase 2: 15-25% improvement with RL optimization - Phase 3: Full meta-learning and self-improvement capabilities

Total Estimated Effort: 200-300 hours across 14-20 weeks

Key Deliverables: - Self-edit generation system - RL optimization framework (optional) - Enhanced AutoTrainingOrchestrator - Comprehensive testing suite - User documentation

Phase 1: Self-Edit Data Generation (Weeks 1-4)¶

Objective: Implement SEAL's synthetic data generation to augment expert training datasets

Effort: 40-60 hours
Risk Level: LOW
Priority: HIGH
Dependencies: None (can start immediately)

Week 1: Foundation & Core Classes¶

1.1 Project Setup¶

Duration: 2-3 hours

[ ] Create directory structure

src/aios/core/seal/
├── __init__.py
├── self_edit_generator.py
├── prompts.py
├── strategies.py
└── cache.py

tests/test_seal/
├── __init__.py
├── test_self_edit_generator.py
├── test_strategies.py
└── fixtures/
    └── sample_passages.txt

[ ] Add SEAL dependencies to requirements
File: pyproject.toml

Add dependencies:

# SEAL Integration
"sentence-transformers>=2.2.0",  # For quality filtering
"rouge-score>=0.1.2",             # For diversity metrics

[ ] Update configuration schema
File: config/default.yaml
Add SEAL configuration section (see below)
[ ] Create feature flag
File: src/aios/core/config/feature_flags.py
Add ENABLE_SEAL_SELF_EDITS = True

1.2 Prompts Module¶

Duration: 3-4 hours
File: src/aios/core/seal/prompts.py

[ ] Define prompt templates

# Implement these prompt strategies from SEAL paper:
- IMPLICATIONS_PROMPT
- IMPLICATIONS_LONG_PROMPT
- IMPLICATIONS_VERY_LONG_PROMPT
- REWRITE_PROMPT
- SELF_QA_PROMPT
- IMPLICATIONS_COT_PROMPT

[ ] Create PromptBuilder class
[ ] Method: build_prompt(passage: str, strategy: str) -> str
[ ] Method: format_system_message() -> str
[ ] Method: format_few_shot_examples() -> List[Dict]
[ ] Validation for unknown strategies
[ ] Add prompt customization
[ ] Support for user-defined prompts
[ ] Prompt template variables (passage, domain, etc.)
[ ] Load prompts from config file
[ ] Write unit tests
[ ] Test all 6 prompt strategies
[ ] Test prompt formatting
[ ] Test edge cases (empty passage, very long passage)

1.3 Strategies Module¶

Duration: 4-5 hours
File: src/aios/core/seal/strategies.py

[ ] Define SelfEditStrategy base class

class SelfEditStrategy(ABC):
    @abstractmethod
    def generate(self, passage: str, model, tokenizer, **kwargs) -> List[str]:
        pass

    @abstractmethod
    def parse_output(self, raw_output: str) -> List[str]:
        pass

[ ] Implement ImplicationsStrategy
[ ] Generate method with temperature sampling
[ ] Parse output by newlines
[ ] Filter empty/invalid implications
[ ] Deduplication logic
[ ] Implement RewriteStrategy
[ ] Multiple rewrite variants
[ ] Parse by delimiter
[ ] Ensure rewrites are different from original
[ ] Implement SelfQAStrategy
[ ] Generate Q&A pairs
[ ] Parse questions and answers separately
[ ] Format as training pairs
[ ] Validate Q&A structure
[ ] Add strategy registry
[ ] Registry dict: strategy_name -> Strategy class
[ ] Factory method: get_strategy(name: str) -> SelfEditStrategy
[ ] Write unit tests
[ ] Test each strategy independently
[ ] Mock model generation
[ ] Test parsing with various formats
[ ] Test edge cases

1.4 Core Generator Class¶

Duration: 6-8 hours
File: src/aios/core/seal/self_edit_generator.py

[ ] Implement SelfEditGenerator class

class SelfEditGenerator:
    def __init__(
        self,
        model: nn.Module,
        tokenizer: PreTrainedTokenizer,
        strategy: str = "implications",
        num_edits: int = 5,
        temperature: float = 1.0,
        max_length: int = 1024,
        cache_dir: Optional[str] = None,
    ):
        # Implementation

[ ] Core generation method
[ ] generate_self_edits(passage: str, **kwargs) -> List[str]
[ ] Batched generation for efficiency
[ ] Error handling for generation failures
[ ] Timeout handling (max 30s per edit)
[ ] GPU memory management
[ ] Quality filtering
[ ] Filter by minimum length (20 tokens)
[ ] Filter by maximum length (2048 tokens)
[ ] Detect and remove duplicates
[ ] Detect and remove low-quality outputs (gibberish)
[ ] Optional: Semantic similarity filtering
[ ] Caching mechanism
[ ] Cache key: hash(passage + strategy + model_name)
[ ] Save to disk: {cache_dir}/{hash}.json
[ ] Load from cache if available
[ ] Cache invalidation strategy
[ ] Max cache size limit (10GB default)
[ ] Logging and telemetry
[ ] Log generation time per passage
[ ] Log cache hit/miss rates
[ ] Log quality filter statistics
[ ] Track total tokens generated
[ ] Write unit tests
[ ] Test with mock model
[ ] Test caching behavior
[ ] Test quality filtering
[ ] Test error handling
[ ] Test with real small model (optional)

1.5 Configuration Integration¶

Duration: 2-3 hours
File: config/default.yaml

[ ] Add SEAL configuration section

seal:
  # Self-Edit Generation
  enabled: true

  # Strategy Selection
  strategy: "implications"  # implications, rewrite, self-qa, implications-long
  num_edits_per_passage: 5
  temperature: 1.0
  max_length: 1024

  # Quality Filtering
  min_length: 20
  max_length: 2048
  enable_deduplication: true
  enable_quality_filter: true
  similarity_threshold: 0.9  # For deduplication

  # Caching
  enable_cache: true
  cache_dir: "artifacts/seal_cache"
  max_cache_size_gb: 10

  # Performance
  batch_size: 4
  max_generation_time: 30  # seconds per edit

  # Advanced (Phase 2)
  enable_rl_optimization: false
  restem_iterations: 2
  restem_batch_size: 50
  restem_candidates_per_passage: 5

[ ] Validation schema
File: src/aios/core/config/schemas.py
Add SEAL config validation
Type checking for all fields
Range validation (e.g., temperature 0.1-2.0)

Week 2: Integration with AutoTrainingOrchestrator¶

2.1 Refactor AutoTrainingOrchestrator¶

Duration: 6-8 hours
File: src/aios/core/training/auto_training_orchestrator.py

[ ] Add SelfEditGenerator to orchestrator

class AutoTrainingOrchestrator:
    def __init__(self, config):
        # ... existing code ...

        if config.seal.enabled:
            self.self_edit_generator = SelfEditGenerator(
                model=self.base_model,
                tokenizer=self.tokenizer,
                strategy=config.seal.strategy,
                num_edits=config.seal.num_edits_per_passage,
                cache_dir=config.seal.cache_dir,
            )
        else:
            self.self_edit_generator = None

[ ] Modify dataset preparation pipeline
[ ] Existing method: prepare_training_data(dataset_path: str)
[ ] Add parameter: augment_with_self_edits: bool = True
[ ] Load original passages
[ ] Generate self-edits for each passage
[ ] Combine original + self-edits
[ ] Return augmented dataset
[ ] Add progress tracking
[ ] Progress bar for self-edit generation
[ ] ETA calculation
[ ] Intermediate saving (every 100 passages)
[ ] Resume capability if interrupted
[ ] Error handling
[ ] Graceful fallback if generation fails
[ ] Retry logic (max 3 retries per passage)
[ ] Log failures for debugging
[ ] Continue with original data if all retries fail
[ ] Write unit tests
[ ] Test augmentation pipeline
[ ] Test with SEAL enabled/disabled
[ ] Test error handling
[ ] Test progress tracking

2.2 Update Expert Training Pipeline¶

Duration: 4-5 hours
File: src/aios/core/training/expert_trainer.py

[ ] Modify train_expert() function
[ ] Add parameter: use_self_edits: bool = True
[ ] Integrate self-edit generation before training
[ ] Log augmented dataset statistics
[ ] Save augmented dataset for inspection

[ ] Dataset statistics logging

def log_dataset_stats(original, augmented):
    # Original dataset size
    # Augmented dataset size
    # Augmentation ratio
    # Average self-edits per passage
    # Total tokens (before/after)

[ ] A/B testing support
[ ] Train two experts: with/without self-edits
[ ] Compare validation metrics
[ ] Generate comparison report
[ ] Save results to artifacts/seal_experiments/
[ ] Write integration tests
[ ] End-to-end training with self-edits
[ ] Verify augmented dataset format
[ ] Verify training completes successfully
[ ] Test A/B comparison workflow

2.3 CLI Integration¶

Duration: 3-4 hours
File: src/aios/cli/aios.py

[ ] Add SEAL commands

# Generate self-edits for a dataset (standalone)
aios seal generate --input dataset.txt --output augmented.txt --strategy implications

# Preview self-edits (no saving)
aios seal preview --input dataset.txt --num-samples 5

# Cache management
aios seal cache clear
aios seal cache stats
aios seal cache prune --max-size 5GB

[ ] Extend train command

# Train expert with self-edits (default)
aios hrm-hf train-expert --expert-id abc123 --dataset data.txt

# Train expert without self-edits
aios hrm-hf train-expert --expert-id abc123 --dataset data.txt --no-seal

# Train with specific strategy
aios hrm-hf train-expert --expert-id abc123 --dataset data.txt --seal-strategy rewrite

[ ] Add verbose logging option
[ ] --seal-verbose flag
[ ] Show generation progress
[ ] Show quality filter statistics
[ ] Show cache hit rates
[ ] Write CLI tests
[ ] Test all new commands
[ ] Test flag combinations
[ ] Test error messages

Week 3: Testing & Validation¶

3.1 Unit Testing¶

Duration: 6-8 hours

[ ] Test coverage targets
[ ] SelfEditGenerator: 90%+ coverage
[ ] Strategies: 85%+ coverage
[ ] Prompts: 80%+ coverage
[ ] Integration: 75%+ coverage
[ ] Specific test cases
[ ] Test each prompt strategy
[ ] Test with various passage lengths
[ ] Test with special characters/unicode
[ ] Test with edge cases (empty, very long)
[ ] Test caching behavior
[ ] Test quality filtering
[ ] Test error handling
[ ] Test memory management
[ ] Mock generation testing
File: tests/test_seal/test_self_edit_generator.py
Mock model.generate() to return predictable outputs
Verify parsing logic
Verify filtering logic
Verify caching logic
[ ] Performance testing
[ ] Measure generation time per passage
[ ] Measure memory usage
[ ] Test batch generation efficiency
[ ] Profile bottlenecks

3.2 Integration Testing¶

Duration: 6-8 hours

[ ] End-to-end workflow tests
File: tests/integration/test_seal_workflow.py
[ ] Test 1: Generate self-edits → Train expert → Validate
[ ] Test 2: Train with/without SEAL → Compare results
[ ] Test 3: Cache persistence across runs
[ ] Test 4: Error recovery and retries
[ ] Real model testing (optional but recommended)
[ ] Use small model (e.g., GPT-2 124M)
[ ] Generate real self-edits
[ ] Verify output quality
[ ] Compare to manual inspection
[ ] Training pipeline integration
[ ] Verify expert training completes
[ ] Verify no regressions in base model
[ ] Verify augmented dataset format
[ ] Verify checkpoint saving

3.3 A/B Experiment Setup¶

Duration: 4-5 hours

[ ] Design experiment
[ ] Select small test dataset (100 passages)
[ ] Split into train/validation
[ ] Define success metrics (see below)
[ ] Implement comparison script

File: scripts/seal_ab_test.py

# Train two experts:
# Control: Standard training
# Treatment: Training with SEAL self-edits

# Compare:
# - Validation loss
# - Perplexity
# - Training time
# - Memory usage

[ ] Create experiment config
File: experiments/seal_phase1_experiment.yaml
Define hyperparameters
Define evaluation metrics
Define comparison criteria
[ ] Run pilot experiment
[ ] Train control expert (no SEAL)
[ ] Train treatment expert (with SEAL)
[ ] Compare results
[ ] Document findings

4.1 User Documentation¶

Duration: 4-5 hours

[ ] Create user guide
File: docs/user_guide/SEAL_SELF_EDITS.md (placeholder; to be created — see docs/INDEX.md and docs/guide/ for current docs)
[ ] What is SEAL?
[ ] How does self-edit generation work?
[ ] When to use self-edits?
[ ] Configuration options explained
[ ] CLI usage examples
[ ] Troubleshooting common issues
[ ] Update existing docs
[ ] Update docs/QUICK_START.md with SEAL mention
[ ] Update docs/user_guide/TRAINING.md with self-edit section (placeholder target; user guide not yet authored)
[ ] Update config/default.yaml with inline comments
[ ] Update README.md with SEAL feature highlight
[ ] Create tutorial
File: docs/tutorials/SEAL_FIRST_EXPERT.md (placeholder; to be created)
[ ] Step-by-step: Train your first expert with SEAL
[ ] Expected results and interpretation
[ ] Comparison: with vs without SEAL

4.2 API Documentation¶

Duration: 3-4 hours

[ ] Generate API docs
[ ] Add docstrings to all public methods
[ ] Use Google/NumPy docstring format
[ ] Include usage examples in docstrings
[ ] Generate Sphinx/MkDocs API reference
[ ] Code examples

File: docs/api/seal_examples.md (placeholder; to be created)

# Example 1: Basic usage
# Example 2: Custom strategy
# Example 3: Advanced configuration
# Example 4: Programmatic access

4.3 Performance Optimization¶

Duration: 4-6 hours

[ ] Profile generation pipeline
[ ] Identify bottlenecks
[ ] Optimize batching
[ ] Optimize tokenization
[ ] Optimize caching
[ ] Memory optimization
[ ] Reduce peak memory usage
[ ] Implement streaming for large datasets
[ ] Add memory usage monitoring
[ ] Test on large datasets (10K+ passages)
[ ] Caching optimization
[ ] Implement cache warming
[ ] Add cache preloading
[ ] Optimize cache lookup
[ ] Test cache performance

4.4 Final Validation¶

Duration: 2-3 hours

[ ] Run full test suite
[ ] All unit tests pass
[ ] All integration tests pass
[ ] Code coverage meets targets
[ ] No critical linting errors
[ ] Manual testing checklist
[ ] Install on fresh environment
[ ] Run through tutorial
[ ] Test all CLI commands
[ ] Generate self-edits for sample dataset
[ ] Train sample expert
[ ] Verify improvement vs baseline
[ ] Performance benchmarks
[ ] Generation time per passage
[ ] Memory usage during generation
[ ] Cache hit rate
[ ] Training time comparison
[ ] Documentation review
[ ] All docs are accurate
[ ] All examples work
[ ] No broken links
[ ] Clear and easy to follow

Phase 1 Checklist Summary¶

Before Starting: - [ ] Review SEAL paper thoroughly - [ ] Review SEAL GitHub code - [ ] Understand AI-OS codebase - [ ] Set up development environment - [ ] Create feature branch: feature/seal-phase1

Week 1 Complete: - [ ] Project structure created - [ ] Dependencies added - [ ] Prompts module implemented and tested - [ ] Strategies module implemented and tested - [ ] SelfEditGenerator class implemented and tested - [ ] Configuration integrated

Week 2 Complete: - [ ] AutoTrainingOrchestrator updated - [ ] Expert training pipeline updated - [ ] CLI commands added - [ ] Integration tests written

Week 3 Complete: - [ ] All unit tests pass (90%+ coverage) - [ ] All integration tests pass - [ ] A/B experiment completed - [ ] Results documented

Week 4 Complete: - [ ] User documentation written - [ ] API documentation generated - [ ] Performance optimized - [ ] Final validation passed - [ ] Ready for merge to main

Phase 1 Acceptance Criteria: - [ ] Self-edit generation works for all strategies - [ ] Expert training with SEAL completes successfully - [ ] 10-15% improvement in validation loss vs baseline - [ ] No regressions in base model or existing features - [ ] Documentation is complete and accurate - [ ] Code coverage >85% - [ ] Performance within acceptable limits (<30s per passage)

Phase 2: RL Optimization (Weeks 5-14)¶

Objective: Implement ReSTEM reinforcement learning to optimize self-edit quality

Effort: 80-120 hours
Risk Level: MEDIUM
Priority: MEDIUM
Dependencies: Phase 1 complete and validated

Note: This phase is OPTIONAL. Only proceed if Phase 1 shows promising results and you have compute budget for RL training.

Week 5-6: ReSTEM Foundation¶

5.1 Research & Planning¶

Duration: 6-8 hours

[ ] Deep dive into ReSTEM
[ ] Read SEAL paper Section 3.1 thoroughly
[ ] Study SEAL GitHub implementation
[ ] Understand E-step (sampling) and M-step (SFT)
[ ] Understand binary reward computation
[ ] Review hyperparameters from paper
[ ] Design AI-OS adaptation
[ ] Define reward function for AI-OS
[ ] Design inner loop (TTT)
[ ] Design outer loop (policy update)
[ ] Plan checkpoint management
[ ] Plan GPU resource allocation
[ ] Create design document
File: docs/development/SEAL_RESTEM_DESIGN.md (placeholder; to be created)
[ ] Architecture diagram
[ ] Algorithm pseudocode
[ ] Resource requirements
[ ] Risk analysis

5.2 Reward Function Implementation¶

Duration: 6-8 hours
File: src/aios/core/seal/reward.py

[ ] Define RewardFunction base class

class RewardFunction(ABC):
    @abstractmethod
    def compute_reward(
        self,
        expert: nn.Module,
        validation_dataset: Dataset,
        threshold: float,
    ) -> float:
        """
        Returns:
            1.0 if expert improves over threshold
            0.0 otherwise
        """
        pass

[ ] Implement ValidationLossReward
[ ] Evaluate expert on validation set
[ ] Compute average loss
[ ] Return 1 if loss < threshold, 0 otherwise
[ ] Cache validation results
[ ] Implement PerplexityReward
[ ] Compute perplexity on validation set
[ ] Return binary reward based on threshold
[ ] Implement AccuracyReward (optional)
[ ] For classification/QA tasks
[ ] Compute accuracy
[ ] Return binary reward
[ ] Add reward computation utilities
[ ] Efficient validation batching
[ ] GPU memory management
[ ] Timeout handling
[ ] Result caching
[ ] Write unit tests
[ ] Test each reward function
[ ] Test with mock expert
[ ] Test edge cases

5.3 Test-Time Training (TTT) Module¶

Duration: 8-10 hours
File: src/aios/core/seal/test_time_training.py

[ ] Implement TTTTrainer class

class TTTTrainer:
    def train_temporary_expert(
        self,
        base_expert: nn.Module,
        self_edit: str,
        epochs: int = 5,
        learning_rate: float = 1e-4,
    ) -> Tuple[nn.Module, Dict]:
        """
        Train expert on self-edit, return trained expert + metrics.
        """

[ ] LoRA integration
[ ] Use PEFT library for efficient adapters
[ ] Configure LoRA rank and alpha
[ ] Apply LoRA to expert module
[ ] Train only LoRA parameters
[ ] Training loop
[ ] Freeze base expert
[ ] Train LoRA adapter
[ ] Monitor loss
[ ] Early stopping if loss plateaus
[ ] Save temporary checkpoint
[ ] Memory management
[ ] Create expert copy for TTT
[ ] Clean up after training
[ ] Garbage collection
[ ] GPU memory clearing
[ ] Write unit tests
[ ] Test TTT with mock expert
[ ] Test LoRA application
[ ] Test memory cleanup
[ ] Test training convergence

Week 7-8: ReSTEM Optimizer Core¶

7.1 ReSTEM Optimizer Implementation¶

Duration: 10-12 hours
File: src/aios/core/seal/restem_optimizer.py

[ ] Implement ReSTEMOptimizer class

class ReSTEMOptimizer:
    def __init__(
        self,
        self_edit_generator: SelfEditGenerator,
        ttt_trainer: TTTTrainer,
        reward_function: RewardFunction,
        num_iterations: int = 2,
        batch_size: int = 50,
        candidates_per_passage: int = 5,
    ):
        # Implementation

[ ] E-Step: Sample self-edits
[ ] Generate N candidates per passage
[ ] Use current generator policy
[ ] Store candidates with metadata
[ ] Log sampling statistics
[ ] Inner Loop: TTT evaluation
[ ] For each candidate self-edit:
- [ ] Train temporary expert via TTT
- [ ] Evaluate on validation set
- [ ] Compute reward
- [ ] Store (candidate, reward) pair
[ ] Parallel processing (if multiple GPUs)
[ ] Progress tracking
[ ] Checkpointing
[ ] M-Step: Policy update
[ ] Filter candidates by reward (keep reward=1)
[ ] Fine-tune generator on good self-edits
[ ] Use standard SFT (AdamW optimizer)
[ ] Monitor training metrics
[ ] Save updated generator checkpoint
[ ] Iteration management
[ ] Run E-M loop for N iterations
[ ] Track metrics across iterations
[ ] Early stopping if no improvement
[ ] Save best generator checkpoint
[ ] Logging and telemetry
[ ] Log per-iteration statistics
[ ] Log reward distribution
[ ] Log generator improvement
[ ] Save to JSONL file

7.2 Integration with Training Pipeline¶

Duration: 6-8 hours
File: src/aios/core/training/auto_training_orchestrator.py

[ ] Add ReSTEM mode to orchestrator

def train_expert_with_restem(
    self,
    expert_id: str,
    dataset_path: str,
    validation_split: float = 0.2,
) -> Dict:
    # Split dataset
    # Initialize ReSTEM optimizer
    # Run optimization loop
    # Train final expert with optimized generator
    # Return metrics

[ ] Resource management
[ ] Estimate GPU memory requirements
[ ] Check available resources
[ ] Allocate GPUs for TTT
[ ] Handle out-of-memory errors
[ ] Checkpoint management
[ ] Save intermediate generator checkpoints
[ ] Save reward statistics
[ ] Save best self-edits
[ ] Resume capability from checkpoint
[ ] Write integration tests
[ ] Test ReSTEM workflow end-to-end
[ ] Test with small mock dataset
[ ] Verify checkpoint saving/loading
[ ] Test error handling

Week 9-10: Testing & Optimization¶

9.1 Comprehensive Testing¶

Duration: 8-10 hours

[ ] Unit tests
[ ] RewardFunction implementations
[ ] TTTTrainer methods
[ ] ReSTEMOptimizer E-step
[ ] ReSTEMOptimizer M-step
[ ] Edge cases and error handling
[ ] Integration tests
[ ] Full ReSTEM loop with mock data
[ ] Multi-GPU TTT if available
[ ] Checkpoint save/load
[ ] Memory cleanup
[ ] Performance tests
[ ] Measure TTT time per candidate
[ ] Measure total ReSTEM time
[ ] Measure memory usage
[ ] Identify bottlenecks

9.2 Hyperparameter Tuning¶

Duration: 6-8 hours

[ ] Create tuning script
File: scripts/tune_restem_hyperparams.py
[ ] Grid search over:
- Learning rate: [1e-5, 3e-5, 1e-4]
- Reward threshold: [auto, fixed values]
- LoRA rank: [8, 16, 32]
- TTT epochs: [3, 5, 10]
[ ] Run tuning experiments
[ ] Use small dataset for speed
[ ] Track validation improvement
[ ] Document best hyperparameters
[ ] Update default config

9.3 Validation Experiment¶

Duration: 8-10 hours

[ ] Design experiment
[ ] Select test domain (e.g., Python programming)
[ ] Prepare dataset (100-200 passages)
[ ] Define success criteria
[ ] Run comparison
[ ] Baseline: Phase 1 (self-edits without RL)
[ ] Treatment: Phase 2 (ReSTEM optimization)
[ ] Measure:
- Validation loss improvement
- Training time
- Compute cost
- Expert quality
[ ] Document results
File: experiments/seal_phase2_results.md
[ ] Quantitative metrics
[ ] Qualitative assessment
[ ] Cost-benefit analysis
[ ] Recommendation for production

Week 11-12: CLI, GUI, and Usability¶

11.1 CLI Commands¶

Duration: 4-5 hours

[ ] Add ReSTEM commands

# Run ReSTEM optimization
aios seal restem optimize \
    --dataset data.txt \
    --output-generator generator.pt \
    --iterations 2 \
    --batch-size 50

# Use optimized generator for training
aios hrm-hf train-expert \
    --expert-id abc123 \
    --dataset data.txt \
    --seal-generator path/to/optimized_generator.pt

[ ] Add monitoring commands

# View ReSTEM progress
aios seal restem status

# View reward statistics
aios seal restem rewards --run-id xyz

# Compare generators
aios seal restem compare --baseline gen1.pt --optimized gen2.pt

11.2 GUI Integration (Optional)¶

Duration: 6-8 hours

[ ] Add ReSTEM panel to Subbrains Manager
Show optimization progress
Display reward statistics
Show best self-edits
Allow starting/stopping optimization
[ ] Visualization
Reward distribution over iterations
Generator improvement metrics
Training progress for TTT
Resource usage graphs

11.3 Documentation¶

Duration: 6-8 hours

[ ] Create ReSTEM guide
File: docs/user_guide/SEAL_RESTEM.md (placeholder; to be created)
[ ] What is ReSTEM?
[ ] When to use it?
[ ] How to configure?
[ ] Interpreting results
[ ] Troubleshooting
[ ] Update existing docs
[ ] Update SEAL overview
[ ] Update training guide
[ ] Add cost estimates
[ ] Add best practices
[ ] Create tutorial
File: docs/tutorials/SEAL_RESTEM_TUTORIAL.md (placeholder; to be created)
[ ] Step-by-step ReSTEM optimization
[ ] Expected results
[ ] Analysis and interpretation

Week 13-14: Production Readiness¶

13.1 Performance Optimization¶

Duration: 8-10 hours

[ ] Profile ReSTEM pipeline
[ ] Identify bottlenecks
[ ] Optimize TTT batching
[ ] Optimize reward computation
[ ] Reduce checkpoint I/O
[ ] Memory optimization
[ ] Reduce peak GPU memory
[ ] Implement gradient accumulation
[ ] Add CPU offloading
[ ] Test with limited resources
[ ] Parallelization
[ ] Multi-GPU TTT
[ ] Parallel candidate evaluation
[ ] Async reward computation
[ ] Test scaling behavior

13.2 Cost Analysis¶

Duration: 3-4 hours

[ ] Compute cost estimation
[ ] Calculate GPU hours for typical run
[ ] Estimate cloud costs
[ ] Compare to Phase 1 baseline
[ ] Document cost-benefit tradeoff
[ ] Create cost calculator
File: scripts/estimate_restem_cost.py
[ ] Input: dataset size, iterations, GPU type
[ ] Output: time and cost estimates
[ ] Recommendations for budget

13.3 Final Validation¶

Duration: 4-5 hours

[ ] Full test suite
[ ] All unit tests pass
[ ] All integration tests pass
[ ] Performance tests pass
[ ] Code coverage >85%
[ ] End-to-end validation
[ ] Fresh environment install
[ ] Run ReSTEM optimization
[ ] Train expert with optimized generator
[ ] Verify improvement vs Phase 1
[ ] Document results
[ ] Production readiness checklist
[ ] Error handling complete
[ ] Logging comprehensive
[ ] Monitoring in place
[ ] Documentation complete
[ ] Performance acceptable
[ ] Costs documented

Phase 2 Checklist Summary¶

Before Starting: - [ ] Phase 1 complete and validated - [ ] Results show 10-15% improvement - [ ] Compute budget approved (2xH100 for 20-40 hours) - [ ] Create feature branch: feature/seal-phase2

Week 5-6 Complete: - [ ] ReSTEM research and design complete - [ ] Reward functions implemented and tested - [ ] TTT module implemented and tested

Week 7-8 Complete: - [ ] ReSTEM optimizer core implemented - [ ] Integration with training pipeline complete - [ ] Basic tests passing

Week 9-10 Complete: - [ ] Comprehensive testing complete - [ ] Hyperparameters tuned - [ ] Validation experiment complete

Week 11-12 Complete: - [ ] CLI commands added - [ ] GUI integration complete (optional) - [ ] Documentation written

Week 13-14 Complete: - [ ] Performance optimized - [ ] Cost analysis complete - [ ] Final validation passed - [ ] Ready for merge

Phase 2 Acceptance Criteria: - [ ] ReSTEM optimization completes successfully - [ ] 15-20% improvement over Phase 1 (total 25-35% over baseline) - [ ] Cost is reasonable (<$100 per expert on cloud) - [ ] No critical bugs or crashes - [ ] Documentation is complete - [ ] Code coverage >85% - [ ] Performance within acceptable limits

Phase 3: Advanced Meta-Learning (Weeks 15+)¶

Objective: Implement advanced SEAL features for full meta-learning capabilities

Effort: 100-150 hours
Risk Level: HIGH
Priority: OPTIONAL (Research Phase)
Dependencies: Phase 2 complete and providing significant value

Note: This phase is HIGHLY OPTIONAL and research-oriented. Only proceed if you have specific use cases and research goals.

Week 15-16: Test-Time Adaptation¶

15.1 Context-Aware Fine-Tuning¶

Duration: 10-12 hours
File: src/aios/core/seal/test_time_adaptation.py

[ ] Implement TTAModule

class TestTimeAdaptation:
    def adapt_to_context(
        self,
        expert: nn.Module,
        conversation_history: List[str],
        adaptation_steps: int = 5,
    ) -> nn.Module:
        """
        Fine-tune expert on recent conversation for better context awareness.
        """

[ ] Context extraction
[ ] Extract relevant passages from conversation
[ ] Generate self-edits from context
[ ] Create mini training set
[ ] Rapid adaptation
[ ] Use small LoRA (rank=4)
[ ] Very low learning rate (1e-5)
[ ] Few gradient steps (5-10)
[ ] Minimal memory overhead
[ ] Integration with chat
[ ] Adapt expert after every N messages
[ ] Track adaptation history
[ ] Option to disable TTA
[ ] Performance monitoring
[ ] Write tests
[ ] Test adaptation logic
[ ] Test memory management
[ ] Test performance impact

15.2 Few-Shot Learning (ARC-Style)¶

Duration: 10-12 hours
File: src/aios/core/seal/few_shot_learning.py

[ ] Implement FewShotLearner
[ ] Based on SEAL's ARC experiments
[ ] Support data augmentation tools
[ ] Support hyperparameter selection
[ ] Generate self-edits with tool invocations
[ ] Tool framework
[ ] Define tool interface
[ ] Implement rotation/flip augmentations
[ ] Implement learning rate selection
[ ] Implement epoch selection
[ ] Integration
[ ] Add to expert training pipeline
[ ] Enable for specific tasks
[ ] Track success rates
[ ] Write tests
[ ] Test tool invocation
[ ] Test augmentation strategies
[ ] Test few-shot scenarios

Week 17-18: Cross-Model Transfer¶

17.1 Expert Portability¶

Duration: 8-10 hours

[ ] Export/import experts
[ ] Export expert to HuggingFace format
[ ] Include metadata and training history
[ ] Include self-edit generator configuration
[ ] Package as distributable artifact
[ ] Cross-model adaptation
[ ] Load expert trained on Model A
[ ] Adapt to Model B architecture
[ ] Fine-tune adapter layer
[ ] Validate performance
[ ] Expert marketplace (concept)
[ ] Design sharing protocol
[ ] Define quality standards
[ ] Create browsing interface
[ ] Implement download/install

17.2 Generator Transfer¶

Duration: 6-8 hours

[ ] Transfer learned strategies
[ ] Export optimized generator
[ ] Import to new domain
[ ] Fine-tune for domain
[ ] Compare to from-scratch training
[ ] Multi-domain optimization
[ ] Train generator on multiple domains
[ ] Test generalization
[ ] Measure transfer learning benefit

Week 19-20: Research Features¶

19.1 Advanced Routing¶

Duration: 8-10 hours

[ ] SEAL-aware routing
[ ] Route based on self-edit quality
[ ] Track which self-edits led to activations
[ ] Use as routing signal
[ ] Meta-routing
[ ] Learn routing from SEAL training
[ ] Optimize routing for self-improvement
[ ] Experiment with routing strategies

19.2 Continual ReSTEM¶

Duration: 8-10 hours

[ ] Background optimization
[ ] Run ReSTEM during idle time
[ ] Incrementally improve generators
[ ] Track long-term improvements
[ ] Online learning
[ ] Update generators from user feedback
[ ] Incorporate conversation quality signals
[ ] Avoid catastrophic forgetting

19.3 Documentation & Publication¶

Duration: 8-10 hours

[ ] Research documentation
File: docs/research/SEAL_METALEARNING.md
[ ] Document novel contributions
[ ] Document experimental results
[ ] Comparison to SEAL paper
[ ] Potential paper/blog post
[ ] "SEAL Integration in HRM Architecture"
[ ] Document challenges and solutions
[ ] Share lessons learned
[ ] Release findings to community

Phase 3 Checklist Summary¶

Before Starting: - [ ] Phase 2 showing strong results (20-30% improvement) - [ ] Research goals clearly defined - [ ] Team has bandwidth for experiments - [ ] Create feature branch: feature/seal-phase3

Week 15-16 Complete: - [ ] Test-time adaptation implemented - [ ] Few-shot learning implemented - [ ] Initial experiments complete

Week 17-18 Complete: - [ ] Expert portability working - [ ] Cross-model transfer validated - [ ] Marketplace concept designed

Week 19-20 Complete: - [ ] Advanced routing experimented - [ ] Continual learning explored - [ ] Research documentation written

Phase 3 Acceptance Criteria: - [ ] Novel features implemented and tested - [ ] Experimental results documented - [ ] Research contributions identified - [ ] Community-shareable findings - [ ] Decision made on production features

Dependencies & Prerequisites¶

System Requirements¶

Phase 1: - [ ] Python 3.10+ - [ ] PyTorch 2.0+ - [ ] Transformers 4.30+ - [ ] 1x GPU with 16GB+ VRAM (RTX 4090, A100, etc.) - [ ] 32GB+ RAM - [ ] 50GB+ disk space

Phase 2 (additional): - [ ] 2x GPU with 24GB+ VRAM (A100, H100) - [ ] 64GB+ RAM - [ ] 200GB+ disk space - [ ] High-speed GPU interconnect (NVLink preferred)

Phase 3 (additional): - [ ] Research compute budget - [ ] Multi-GPU cluster (optional) - [ ] Extended storage (1TB+)

Software Dependencies¶

Phase 1:

[project.dependencies]
# Existing
torch = ">=2.0.0"
transformers = ">=4.30.0"
# New for SEAL
sentence-transformers = ">=2.2.0"
rouge-score = ">=0.1.2"

Phase 2 (additional):

peft = ">=0.5.0"  # LoRA
accelerate = ">=0.21.0"  # Multi-GPU
wandb = ">=0.15.0"  # Optional: experiment tracking

Phase 3 (additional):

datasets = ">=2.14.0"  # HuggingFace datasets
huggingface-hub = ">=0.16.0"  # Model/expert sharing

Knowledge Prerequisites¶

Phase 1: - [ ] Understanding of AI-OS codebase - [ ] Familiarity with PyTorch and Transformers - [ ] Experience with text generation - [ ] Understanding of prompt engineering

Phase 2: - [ ] Understanding of reinforcement learning basics - [ ] Familiarity with policy gradient methods - [ ] Experience with multi-GPU training - [ ] Understanding of LoRA and PEFT

Phase 3: - [ ] Advanced RL knowledge - [ ] Meta-learning concepts - [ ] Transfer learning experience - [ ] Research methodology

Success Metrics¶

Phase 1 Metrics¶

Primary Metrics: - [ ] Expert Validation Loss: 10-15% improvement over baseline - [ ] Expert Perplexity: Corresponding improvement - [ ] Training Time: <2x overhead for augmentation - [ ] Generation Time: <30s per passage per edit

Secondary Metrics: - [ ] Cache Hit Rate: >50% on repeated datasets - [ ] Quality Filter Rate: <20% filtered outputs - [ ] User Satisfaction: Qualitative assessment - [ ] Code Coverage: >85%

Success Criteria: - [ ] At least 10% improvement in expert quality - [ ] No regressions in existing features - [ ] Generation time acceptable (<30s/edit) - [ ] System stable and reliable

Phase 2 Metrics¶

Primary Metrics: - [ ] Expert Validation Loss: 15-25% improvement over Phase 1 - [ ] Cumulative Improvement: 25-35% over original baseline - [ ] ReSTEM Time: <40 GPU-hours per expert - [ ] Reward Convergence: Improvement within 2 iterations

Secondary Metrics: - [ ] Generator Quality: Surpass GPT-4.1 synthetic data - [ ] Cost per Expert: <$100 on cloud - [ ] Iteration Efficiency: >20% rewards=1 after iteration 1 - [ ] Generalization: Strategy transfers to new domains

Success Criteria: - [ ] At least 15% improvement over Phase 1 - [ ] Compute cost is reasonable - [ ] Generator quality measurably improves - [ ] Strategy generalizes across domains

Phase 3 Metrics¶

Research Metrics: - [ ] Novel Contributions: At least 2 novel techniques - [ ] Experimental Validation: Positive results in 3+ experiments - [ ] Transfer Learning: >10% benefit from cross-domain transfer - [ ] Adaptation Speed: <5 steps for context adaptation

Success Criteria: - [ ] At least one feature ready for production - [ ] Research findings documented and shareable - [ ] Clear roadmap for future work - [ ] Community interest and feedback

Risk Management¶

Phase 1 Risks¶

Risk	Probability	Impact	Mitigation
Self-edits are low quality	Medium	High	Use SEAL's proven prompts, add quality filtering, A/B test
Performance overhead too high	Low	Medium	Optimize batching, use caching, make optional
Integration breaks existing features	Low	High	Comprehensive testing, feature flags, gradual rollout
Limited improvement (<10%)	Medium	Medium	Try multiple strategies, tune hyperparameters, document honestly

Phase 2 Risks¶

Risk	Probability	Impact	Mitigation
RL doesn't converge	Medium	High	Use SEAL hyperparameters, extensive tuning, fallback to Phase 1
Compute cost too high	Medium	Medium	Estimate upfront, optimize efficiency, set budget limits
Memory issues	Medium	High	Test on smaller scales, implement gradient accumulation, CPU offload
No improvement over Phase 1	Low	Medium	Validate Phase 1 first, careful experiment design

Phase 3 Risks¶

Risk	Probability	Impact	Mitigation
Features too experimental	High	Low	Clear research goals, document failures, learn from experiments
No clear production value	Medium	Medium	Focus on specific use cases, validate with users
Resource drain	Medium	Medium	Set time/budget limits, periodic review

Testing Strategy¶

Unit Testing¶

Target Coverage: 85-90%

Phase 1: - [ ] Test each prompt strategy - [ ] Test self-edit parsing - [ ] Test quality filtering - [ ] Test caching logic - [ ] Test error handling - [ ] Mock model generation

Phase 2: - [ ] Test reward computation - [ ] Test TTT training - [ ] Test ReSTEM E-step - [ ] Test ReSTEM M-step - [ ] Test checkpoint management

Phase 3: - [ ] Test adaptation logic - [ ] Test transfer learning - [ ] Test advanced features

Integration Testing¶

Phase 1: - [ ] End-to-end generation pipeline - [ ] Training with augmented data - [ ] Cache persistence - [ ] CLI commands

Phase 2: - [ ] Full ReSTEM optimization - [ ] Multi-GPU coordination - [ ] Checkpoint save/load - [ ] Resource cleanup

Phase 3: - [ ] Cross-model transfer - [ ] Online adaptation - [ ] Complex workflows

Performance Testing¶

Benchmarks: - [ ] Generation time per passage - [ ] Memory usage during generation - [ ] TTT time per candidate (Phase 2) - [ ] End-to-end training time - [ ] GPU utilization

Targets: - [ ] Phase 1: <30s per edit - [ ] Phase 2: <40 GPU-hours per expert - [ ] Memory: <90% GPU utilization - [ ] No memory leaks over long runs

Manual Testing¶

Phase 1: - [ ] Generate self-edits for sample data - [ ] Inspect output quality - [ ] Train sample expert - [ ] Verify improvement - [ ] Test all CLI commands

Phase 2: - [ ] Run ReSTEM on small dataset - [ ] Monitor GPU usage - [ ] Verify improvement over Phase 1 - [ ] Test checkpoint recovery

Phase 3: - [ ] Test experimental features - [ ] Validate research hypotheses - [ ] User testing (if applicable)

Documentation Requirements¶

User Documentation¶

Phase 1: - [ ] docs/user_guide/SEAL_OVERVIEW.md - What is SEAL? (placeholder) - [ ] docs/user_guide/SEAL_SELF_EDITS.md - How to use self-edits (placeholder) - [ ] docs/tutorials/SEAL_FIRST_EXPERT.md - Tutorial (placeholder) - [ ] config/default.yaml - Inline comments for SEAL config - [ ] README.md - Feature highlight

Phase 2: - [ ] docs/user_guide/SEAL_RESTEM.md - ReSTEM guide (placeholder) - [ ] docs/tutorials/SEAL_RESTEM_TUTORIAL.md - Step-by-step (placeholder) - [ ] docs/user_guide/SEAL_COST_ANALYSIS.md - Cost estimation (placeholder) - [ ] Update existing docs with Phase 2 info

Phase 3: - [ ] docs/research/SEAL_METALEARNING.md - Research documentation (placeholder) - [ ] docs/advanced/SEAL_ADVANCED_FEATURES.md - Advanced usage (placeholder) - [ ] Case studies and examples

API Documentation¶

All Phases: - [ ] Comprehensive docstrings (Google format) - [ ] Type annotations - [ ] Usage examples in docstrings - [ ] API reference (Sphinx/MkDocs) - [ ] Code examples in docs/api/

Developer Documentation¶

Phase 1: - [ ] docs/development/SEAL_ARCHITECTURE.md - Architecture overview (placeholder) - [ ] docs/development/SEAL_CONTRIBUTING.md - How to contribute (placeholder) - [ ] Inline code comments for complex logic

Phase 2: - [ ] docs/development/SEAL_RESTEM_DESIGN.md - ReSTEM design doc (placeholder) - [ ] docs/development/SEAL_TESTING.md - Testing guide (placeholder) - [ ] Performance optimization notes

Phase 3: - [ ] Research methodology - [ ] Experiment protocols - [ ] Lessons learned

Rollout Plan¶

Phase 1 Rollout¶

Stage 1: Internal Testing (Week 4) - [ ] Merge to develop branch - [ ] Deploy to staging environment - [ ] Internal team testing - [ ] Bug fixes and refinements

Stage 2: Beta Release (Week 5) - [ ] Release as beta feature - [ ] Feature flag: seal.enabled = false (opt-in) - [ ] Gather user feedback - [ ] Monitor performance and errors

Stage 3: General Availability (Week 6) - [ ] Enable by default: seal.enabled = true - [ ] Announce in release notes - [ ] Promote in documentation - [ ] Monitor adoption and satisfaction

Phase 2 Rollout¶

Stage 1: Internal Validation (Week 13-14) - [ ] Validate on internal datasets - [ ] Compare costs and benefits - [ ] Document best practices - [ ] Create cost calculator

Stage 2: Opt-In Release (Week 15) - [ ] Release as advanced feature - [ ] Require explicit opt-in - [ ] Provide cost estimates - [ ] Gather feedback from power users

Stage 3: Production (Week 16+) - [ ] Enable for users who need it - [ ] Provide clear documentation - [ ] Ongoing monitoring and optimization

Phase 3 Rollout¶

Research Preview: - [ ] Release as experimental features - [ ] Clear "research preview" labeling - [ ] Gather feedback and data - [ ] Decide which features to productionize

Ongoing Maintenance¶

Post-Release Tasks¶

Phase 1: - [ ] Monitor cache usage and performance - [ ] Collect user feedback - [ ] Track success metrics - [ ] Fix bugs and issues - [ ] Add new prompt strategies based on feedback

Phase 2: - [ ] Monitor ReSTEM usage and costs - [ ] Optimize performance based on usage patterns - [ ] Tune hyperparameters for common domains - [ ] Update documentation with best practices

Phase 3: - [ ] Evaluate research features - [ ] Productionize successful experiments - [ ] Deprecate failed experiments - [ ] Plan future research directions

Continuous Improvement¶

[ ] Regular performance profiling
[ ] Dependency updates
[ ] Security patches
[ ] Documentation updates
[ ] Community engagement

Appendices¶

A. Useful Commands¶

# Phase 1: Generate self-edits
aios seal generate --input data.txt --output augmented.txt --strategy implications

# Phase 1: Train expert with SEAL
aios hrm-hf train-expert --expert-id abc123 --dataset data.txt

# Phase 1: A/B test
python scripts/seal_ab_test.py --dataset data.txt --output results.json

# Phase 2: Run ReSTEM
aios seal restem optimize --dataset data.txt --iterations 2

# Phase 2: Monitor progress
aios seal restem status

# Cache management
aios seal cache stats
aios seal cache clear
aios seal cache prune --max-size 5GB

B. Key Files¶

Phase 1: - src/aios/core/seal/self_edit_generator.py - src/aios/core/seal/prompts.py - src/aios/core/seal/strategies.py - src/aios/core/training/auto_training_orchestrator.py - config/default.yaml (SEAL section)

Phase 2: - src/aios/core/seal/restem_optimizer.py - src/aios/core/seal/reward.py - src/aios/core/seal/test_time_training.py

Phase 3: - src/aios/core/seal/test_time_adaptation.py - src/aios/core/seal/few_shot_learning.py - src/aios/core/seal/transfer_learning.py

C. References¶

SEAL Paper: https://arxiv.org/html/2506.10943v2
SEAL GitHub: https://github.com/Continual-Intelligence/SEAL
AI-OS Docs: docs/INDEX.md
HRM Paper: https://arxiv.org/html/2506.21734v3
Dynamic Subbrains: docs/DYNAMIC_SUBBRAINS_ARCHITECTURE.md (placeholder; doc not yet created)

Revision History¶

Version	Date	Author	Changes
1.0	January 15, 2025	AI-OS Team	Initial detailed implementation plan

Sign-Off¶

Phase 1 Ready to Start: ☐ Yes ☐ No
Phase 2 Approved: ☐ Yes ☐ No ☐ Pending Phase 1 Results
Phase 3 Approved: ☐ Yes ☐ No ☐ Research Only

Project Lead Signature: __
Date: __

END OF DOCUMENT

This plan is a living document. Update as implementation progresses and new insights are gained.

Self-Adapting Language Models (SEAL)¶

📋 Table of Contents¶

Executive Summary¶

Phase 1: Self-Edit Data Generation (Weeks 1-4)¶

Week 1: Foundation & Core Classes¶

1.1 Project Setup¶

1.2 Prompts Module¶

1.3 Strategies Module¶

1.4 Core Generator Class¶

1.5 Configuration Integration¶

Week 2: Integration with AutoTrainingOrchestrator¶

2.1 Refactor AutoTrainingOrchestrator¶

2.2 Update Expert Training Pipeline¶

2.3 CLI Integration¶

Week 3: Testing & Validation¶

3.1 Unit Testing¶

3.2 Integration Testing¶

3.3 A/B Experiment Setup¶

Week 4: Documentation & Refinement¶

4.1 User Documentation¶

4.2 API Documentation¶

4.3 Performance Optimization¶

4.4 Final Validation¶

Phase 1 Checklist Summary¶

Phase 2: RL Optimization (Weeks 5-14)¶

Week 5-6: ReSTEM Foundation¶

5.1 Research & Planning¶

5.2 Reward Function Implementation¶

5.3 Test-Time Training (TTT) Module¶

Week 7-8: ReSTEM Optimizer Core¶

7.1 ReSTEM Optimizer Implementation¶

7.2 Integration with Training Pipeline¶

Week 9-10: Testing & Optimization¶

9.1 Comprehensive Testing¶

9.2 Hyperparameter Tuning¶

9.3 Validation Experiment¶

Week 11-12: CLI, GUI, and Usability¶

11.1 CLI Commands¶

11.2 GUI Integration (Optional)¶

11.3 Documentation¶

Week 13-14: Production Readiness¶

13.1 Performance Optimization¶

13.2 Cost Analysis¶

13.3 Final Validation¶

Phase 2 Checklist Summary¶

Phase 3: Advanced Meta-Learning (Weeks 15+)¶

Week 15-16: Test-Time Adaptation¶

15.1 Context-Aware Fine-Tuning¶

15.2 Few-Shot Learning (ARC-Style)¶

Week 17-18: Cross-Model Transfer¶

17.1 Expert Portability¶

17.2 Generator Transfer¶

Week 19-20: Research Features¶

19.1 Advanced Routing¶

19.2 Continual ReSTEM¶

19.3 Documentation & Publication¶

Phase 3 Checklist Summary¶

Dependencies & Prerequisites¶

System Requirements¶

Software Dependencies¶

Knowledge Prerequisites¶

Success Metrics¶

Phase 1 Metrics¶

Phase 2 Metrics¶

Phase 3 Metrics¶

Risk Management¶

Phase 1 Risks¶

Phase 2 Risks¶

Phase 3 Risks¶

Testing Strategy¶

Unit Testing¶

Integration Testing¶

Performance Testing¶

Manual Testing¶

Documentation Requirements¶

User Documentation¶

API Documentation¶

Developer Documentation¶

Rollout Plan¶

Phase 1 Rollout¶