无需重训，测试时用奖励加权引导即可优化自回归模型生成结果。
Reward Weighted Classifier-Free Guidance as Policy Improvement in Autoregressive Models
- 用奖励加权的无分类器引导实现采样分布动态调整。
- 在分子生成任务中，测试时优化新奖励函数，效果优于传统方法。
- 可作教师模型加速强化学习收敛，适合需快速适配新目标的场景。
考虑一个自回归模型生成输出 x（如问答答案、分子），其可通过属性向量 y（如有用性与危害性、生物可用性与亲脂性）描述。任意奖励函数 r(y) 可编码这些属性间的权衡。通常通过强化学习在训练阶段调整采样分布以提升奖励，但若奖励函数改变，则需重新训练。本文提出一种奖励加权的无分类器引导（RCFG），可作为策略改进算子，在不重训的情况下逼近由 Q 函数驱动的分布倾斜。我们将 RCFG 应用于分子生成，证明其可在测试时优化新奖励函数。此外，将 RCFG 作为教师模型进行知识蒸馏，能显著加快标准强化学习的收敛速度。
原文摘要 · Abstract (English)
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 requires re-training. In this paper, we show that a reward weighted classifier-free guidance (RCFG) can act as a policy improvement operator in this setting, approximating tilting the sampling distribution by the Q function. We apply RCFG to molecular generation, demonstrating that it can optimize novel reward functions at test time. Finally, we show that using RCFG as a teacher and distilling into the base policy to serve as a warm start significantly speeds up convergence for standard RL.
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