Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning

AI Digest - ArXiv AI

Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning

Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its behavior from instructions and demonstrations. Policy Iteration with Human Feedback (PIHF) builds on this development and the recurrent evaluate-and-improve structure of generalized policy iteration. PIHF uses a pretrained language model as its execution substrate and moves persistent revision to a versioned natural-lan

Source: ArXiv AI