The Rise of Verbal Reinforcement Learning
This survey separates verbal feedback by when it acts and what it changes, helping agent builders distinguish task specification, test-time guidance, and parameter updates.
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.
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.
This supplies a design vocabulary for feedback-driven agents: language can specify the task, alter reasoning within a run, or update the model afterward. Against prior harness and memory work, it clarifies that retaining feedback as context is only one intervention class and should not be conflated with parameter learning. It organizes implementation choices but does not resolve which feedback mechanism best addresses a given reliability gap.