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Despite their unprecedented success, even the largest language models makemistakes. Similar to how humans learn and improve using feedback, previous workproposed providing language models with natural language feedback to guide themin repairing their outputs. Because human-generated critiques are expensive toobtain, researchers have devised learned critique generators in lieu of humancritics while assuming one can train downstream models to utilize generatedfeedback. However, this approach does not apply to black-box or limited accessmodels such as ChatGPT, as they cannot be fine-tuned. Moreover, in the era oflarge general-purpose language agents, fine-tuning is neither computationallynor spatially efficient as it results in multiple copies of the network. Inthis work, we introduce RL4F (Reinforcement Learning for Feedback), amulti-agent collaborative framework where the critique generator is trained tomaximize end-task performance of GPT-3, a fixed model more than 200 times itssize. RL4F produces critiques that help GPT-3 revise its outputs. We studythree datasets for action planning, summarization and alphabetization and showrelative improvements up to 10% in multiple text similarity metrics over otherlearned, retrieval-augmented or prompting-based critique generators.

RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs