← 研踪 PaperTrailarXiv:2611.01322 · v1
Alignment Flywheel-Guided Suppression Patching for Interactionless Inverse Reinforcement Learning
首次提交 2026-11-01 06:45
版本更新 2026-11-01 06:45
雷达观测 2026-11-04 23:43
摘要
Learned reward models allow reinforcement-learning agents to use demonstrations, implicit feedback, safety constraints, social norms, or other sources that do not provide a complete hand-designed scalar reward. However, a learned reward model can assign high reward to unwanted behavior. A policy can exploit these errors during training. Later requirements can also identify additional regions where the deployed reward should change. We introduce \emph{flywheel-guided patching}, a mechanism for making such localized corrections without retraining the learned evaluator. An Alignment Flywheel identifies overgeneralized or constraint-violating regions and compiles verified findings into suppression patches. We instantiate the mechanism for Interactionless Inverse Reinforcement Learning using MoE-GUIDE, placing patches in the model's latent space and retrieving only a small local subset during online reward evaluation. In the main MuJoCo benchmark, we evaluate patching without expert-state reset warm starts. We also test whether operator-specified norms absent from the demonstrations can be compiled into the same reward interface and compare this with enforcing the corresponding rules online. The results show that localized corrective information can modify an existing learned reward without providing a complete replacement reward. Compilation can reuse judgments that are expensive or unavailable during control. Cheap deterministic rules may be simpler to check directly at each step. Code for MoE-GUIDE, Alignment Flywheel, and flywheel patching is at https://github.com/decide-ugent/Alignment-Flywheel.
作者备注：Accepted for The 38th Benelux Conference on Artificial Intelligence and the 35th Belgian Dutch Conference on Machine Learning
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v1 · 2026-11-01 06:45 · 最新观测版本
Alignment Flywheel-Guided Suppression Patching for Interactionless Inverse Reinforcement Learning
Learned reward models allow reinforcement-learning agents to use demonstrations, implicit feedback, safety constraints, social norms, or other sources that do not provide a complete hand-designed scalar reward. However, a learned reward model can assign high reward to unwanted behavior. A policy can exploit these errors during training. Later requirements can also identify additional regions where the deployed reward should change. We introduce \emph{flywheel-guided patching}, a mechanism for making such localized corrections without retraining the learned evaluator. An Alignment Flywheel identifies overgeneralized or constraint-violating regions and compiles verified findings into suppression patches. We instantiate the mechanism for Interactionless Inverse Reinforcement Learning using MoE-GUIDE, placing patches in the model's latent space and retrieving only a small local subset during online reward evaluation. In the main MuJoCo benchmark, we evaluate patching without expert-state reset warm starts. We also test whether operator-specified norms absent from the demonstrations can be compiled into the same reward interface and compare this with enforcing the corresponding rules online. The results show that localized corrective information can modify an existing learned reward without providing a complete replacement reward. Compilation can reuse judgments that are expensive or unavailable during control. Cheap deterministic rules may be simpler to check directly at each step. Code for MoE-GUIDE, Alignment Flywheel, and flywheel patching is at https://github.com/decide-ugent/Alignment-Flywheel.
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