SelfAlign
AI that learns to align itself.
Why SelfAlign?
The Alignment Problem Is Urgent
As large language models grow more powerful, so does the risk of misalignment—where an AI's behavior subtly or dramatically deviates from human intent. Current alignment methods rely heavily on human feedback, require extensive manual tuning, and often reflect the values of whoever trained the model, rather than those of the user. This makes it hard to trust these systems, especially in high-stakes or cross-cultural settings.
SelfAlign explores a different path.
Instead of hand-crafting alignment through static rules or curated labels, SelfAlign aims to build AI systems that can learn, monitor, and adjust their own alignment behavior over time.
Custom Alignment, Minimal Oversight
SelfAlign is built to give users control—letting them define a model's persona, values, and behavioral constraints, and then train models to follow those instructions through synthetic supervised fine-tuning (SFT) and self-RLHF. It reduces dependence on expensive human feedback loops by generating, filtering, and optimizing its own alignment data.
Lightweight, Adaptable, Auditable
By using LoRA/QLoRA adapters, SelfAlign makes it easy to steer alignment in a modular, parameter-efficient way. These adapters can be plugged into existing LLMs to reflect different user values or application needs. And with built-in tools for tracking alignment drift, value generalization, and ethical compliance, the system remains transparent and accountable during training and deployment.
Toward Safer, More Responsible AI
SelfAlign provides a research framework to test alignment strategies under real constraints: How do we align models at scale, without supervision? How can we ensure alignment holds under stress, ambiguity, or cultural variation? What mechanisms help AI systems notice and correct their own misalignment? These are the questions SelfAlign was built to explore. Because safe AI shouldn't just follow instructions—it should understand why they matter.
Self-Alignment Training Loop
A continuous process where AI systems learn to align themselves through synthetic data generation, self-filtering, and iterative improvement.
Define Values & Persona
Configure desired model behavior
Generate Synthetic Data
Create training examples from values
Fine-Tune on Data
Learn from synthetic examples
Self-Optimize Responses
Select preferred outputs
Track Alignment Drift
Monitor value consistency