Legacy Concept Lab
Deliberative Alignment
Trains models on explicit specifications rather than implicit reward shaping
#85DeliberativeScaling & Alignment
key equation
\max_\pi \mathbb{E}[r_{\text{help}}] + \lambda \mathbb{E}[v_S]Phase 12: Advanced alignment & safety researchConcept 85 of 100
Why It Matters for Modern Models
- Trains models on explicit specifications rather than implicit reward shaping
- Enables auditability: which policy clauses were consulted?
- Reduces over-refusal while improving jailbreak robustness
What Tutorials Skip
What is still poorly explained in textbooks and papers:
- Model retrieves relevant policy text, reasons about it, then responds
- Like Constitutional AI but with explicit spec document in context
- Pareto frontier: helpfulness vs safety vs over-refusal
Interactive Visualization
Core Math (Optional Deep Dive)
If you want intuition first, start with the key equation and the visualization. Come back here for the full walkthrough.
Key Equation
Train model to reason over safety specifications :
Constrained optimization view:
Lagrangian form:
where scores compliance with spec text .