The Rise of Verbal Reinforcement Learning

The survey defines **Verbal Reinforcement Learning** as improving agents through natural-language feedback. It organizes prior work by when feedback takes effect and what it changes, producing **three pillars** spanning task definition, inference-time reasoning, and training.

Use the taxonomy to specify your feedback loop before implementing it: decide whether language should define goals and rewards, steer deliberation during a run, or become training data that changes parameters. Those choices imply different storage, evaluation, and update mechanisms.

This is a unified account of an emerging field, not a benchmark establishing which pillar works best. The abstract outlines categories, challenges, and opportunities but provides no comparative performance or deployment guidance.