Self-Evolution
Agent-native loops that let models keep improving after deployment.
An agent-native loop where the LLM is the decision-maker that keeps a model improving with minimal human intervention.
- Closed cycle: diagnose → hypothesize → pilot → gate → train → reflect.
- Cheap pilot runs are gated before committing to full training, so compute is spent only on promising directions.
- An append-only lesson memory turns failed runs into reusable knowledge for the next cycle.
Built during my AI research internship at TikTok (ByteDance) as a self-evolving training platform.