Chart pathways beyond static language models by integrating continual learning, hybrid architectures, and on-the-job adaptation.
| Pillar | Goal | Techniques |
|---|---|---|
| Plasticity | Incorporate new knowledge rapidly | Online gradient updates, rehearsal buffers, meta-learning |
| Stability | Preserve prior knowledge without catastrophic forgetting | Elastic weight consolidation, orthogonal gradients, parameter isolation |
| Alignment | Maintain safe, regulated behavior during adaptation | Safety filters, human oversight, constrained updates |
| Evaluation | Detect regressions and monitor progress | Streaming benchmarks, delayed reward signals, human audits |
Balancing these pillars is the central challenge of continual learning design.