Following the Text Gradient at Scale
Summary
The post introduces Feedback Descent, a domain-agnostic, text-based optimization loop that replaces scalar rewards with rich textual feedback and an editor that iteratively revises top candidates. It demonstrates the approach with molecular design, SVG optimization, and prompt tuning, arguing that accumulated textual feedback can drive continual improvement without task-specific optimizers.