Reward Weighted Classifier-Free Guidance as Policy Improvement in Autoregressive Models

Consider an auto-regressive model that produces outputs x (e.g., answers to questions, molecules) each of which can be summarized by an attribute vector y (e.g., helpfulness vs. harmlessness, or bio-availability vs. lipophilicity). An arbitrary reward function r(y) encodes tradeoffs between these properties. Typically, tilting the model's sampling distribution to increase this reward is done at training time via reinforcement learning. However, if the reward function changes, re-alignment requir

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ethics.ai (16 April 2026), “Reward Weighted Classifier-Free Guidance as Policy Improvement in Autoregressive Models,” evidence record 5753, https://ethics.ai/record/5753 (originally published by arXiv).

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