akaszubski/realign-meta-framework

Realignment Meta-Framework

Shared framework for all realignment training workflows. Provides the common pipeline template, quality thresholds, and performance optimization guidance used across all domain-specific realignment workflows.

7-Stage Pipeline Template

All realignment workflows follow this common pipeline:

1. Capability Assessment: Evaluate current model capabilities and identify gaps
2. Data Preparation: Collect and prepare domain-specific training data
3. SFT Preparation: Supervised fine-tuning on curated examples
4. Preference/Reward Modeling: Domain-specific optimization (DPO, RLVR, SRF, etc.)
5. Iterative Training: Multi-round training with quality gates
6. Evaluation & Monitoring: Comprehensive evaluation against baselines
7. Deployment & Validation: Final validation and deployment readiness

Quality Thresholds

| Metric | Minimum | Target | Critical | |--------|---------|--------|----------| | Task accuracy | 85% | 92% | < 80% triggers rollback | | Capability retention | 95% | 98% | < 90% triggers rollback | | Data quality score | 0.8 | 0.9 | < 0.7 blocks training | | Evaluation coverage | 80% | 95% | < 70% blocks deployment |

Capability Regression Detection

• Run baseline evaluation suite before and after each training stage
• Track per-capability scores across training rounds
• Automatic rollback if any capability drops > 5% from baseline
• Cross-domain contamination checks between training stages

Performance Optimization

Memory Management

• Use gradient checkpointing for models > 7B parameters
• Batch size auto-tuning based on available memory
• Mixed precision training (fp16/bf16) by default

Training Efficiency

• Learning rate warmup: 5-10% of total steps
• Cosine annealing schedule with min_lr = 0.1 * max_lr
• Early stopping with patience = 3 evaluation rounds
• Checkpoint every N steps (configurable per domain)

Hardware Considerations

• See mlx-performance skill for Apple Silicon optimization
• GPU memory estimation: model_params * 4 bytes * 3 (model + optimizer + gradients)
• Multi-device training coordination patterns

Cross-References

• Hardware details: See mlx-performance skill
• Domain workflows: See realign-domain-workflows skill
• Data quality: See preference-data-quality skill