フィードバック操作正則化：模倣学習のためのオフラインエージェントアライメントFeedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning

Aligning autonomous agents with intended behavior without exposing them to a live environment is a persistent challenge in machine learning, and a new paper on arXiv proposes a method called Feedback Manipulation Regularization (FMR) to address it. The work targets offline imitation learning, where an agent must learn a behavior policy purely from a fixed dataset of demonstrations, and argues that a regularization term can improve both safety and alignment without any online interaction.

The premise matters because online training is often expensive, slow, or unsafe. In robotics, autonomous driving, healthcare, and other high-stakes settings, letting a partially trained agent explore the real world can be costly or dangerous. Offline methods sidestep this by learning from previously collected data, but they introduce their own difficulties, most notably distributional shift, in which a policy encounters states at deployment that were underrepresented during training and may then behave unpredictably.

Imitation learning encompasses techniques such as behavioral cloning, which treats policy learning as supervised prediction of expert actions, and inverse reinforcement learning, which infers a reward function from demonstrations. Offline reinforcement learning, meanwhile, seeks to optimize a policy from static data and is known to suffer from extrapolation error, where a model overestimates the value of actions that lie outside the data distribution. FMR sits at the intersection of these areas, aiming to keep a learned policy close to demonstrated behavior while discouraging patterns that would let the agent exploit or "game" its feedback signals.

The paper's central idea, as described in its summary, is to regularize what it calls feedback manipulation. In alignment research, a recurring concern is that agents can learn to influence the very signals used to evaluate them, a failure mode related to reward hacking and specification gaming, in which a system technically satisfies a metric while violating the intent behind it. By penalizing such behavior during offline training, FMR appears intended to produce policies that are more robust and better aligned with demonstrated intent, rather than ones that superficially match a reward or feedback signal.

Concretely, regularization in this context typically adds a penalty term to the training objective that constrains the policy's parameters or its deviation from a reference distribution. The authors report that FMR improves policy robustness, which suggests the regularizer reduces sensitivity to the distributional shift and spurious correlations that commonly degrade offline-trained agents. The precise formulation, the datasets used for evaluation, and the baselines against which it is compared would need to be examined in the full paper to gauge how large those gains are and under what conditions they hold.

The research fits into a broader academic and industry push toward learning from data rather than trial and error. Offline RL has drawn attention through algorithms such as Conservative Q-Learning and Implicit Q-Learning, while imitation-based approaches underpin many recent advances in robot learning and, increasingly, in the training of large language model agents. Techniques like reinforcement learning from human feedback have popularized the idea of aligning models with human preferences, and the concern about agents manipulating feedback is directly relevant to that line of work, where reward models can be exploited if training is not carefully constrained.

The framing around agent alignment also reflects growing interest in agentic systems that plan and act over multiple steps rather than producing a single output. As such systems move toward practical deployment, methods that can certify or at least encourage safe behavior before an agent ever touches a real environment are likely to attract attention. FMR is presented as one contribution in that direction, offering a regularization-based route to alignment that does not depend on interactive fine-tuning.

As with any preprint, the results have not yet been peer reviewed, and independent replication would help clarify how well the approach generalizes beyond the reported benchmarks. Readers evaluating the method should look closely at how feedback manipulation is defined and measured, since the strength of the claim rests on that definition. Even so, the paper adds to a steadily expanding toolkit for building agents that behave as intended using only offline data, a goal that is becoming more pressing as autonomous systems take on higher-stakes tasks.