realign-meta-frameworklisted

# 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