Continual Learning in Transition
- Published
- Source
- arXiv
- Paper number
- 847
- Field
- Machine Learning
- arXiv ID
- 2608.06216
Key points
- It argues that continual learning used to mean only parameter updates, but now it should also include elements outside the model, such as memory, skills, and protocols.
- It proposes a three-axis framework, When, Where, and How, so that dozens of existing methods can be compared on a single map.
- It summarizes that on-policy learning, where the model learns from its own experience, is better than off-policy methods for reducing forgetting.
- It analyzes verifiable reward as the key bridge that lets agents improve themselves without human help.
- It separates areas where research is dense from areas that are nearly empty, pointing out that system-level integrated learning is the main challenge ahead.
Paper links
External research summaries. These are not HDATF publications or measured product results.