Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Benjamin Poole and Minwoo Lee, in their paper submitted on 8 Jul 2026, introduce Feedback Manipulation Regularization (FMR), an algorithm-agnostic method designed to enhance the alignment of imitation learning policies. FMR utilizes evaluative feedback as a corrective signal for single-stage offline training in fully sequential decision-making environments, contrasting with existing multi-stage pipelines often used for contextual bandit language generation. The researchers adapted Safety Gymnasium environments to serve as a testbed for alignment evaluation. Their findings demonstrate that FMR significantly improves agent aptitude and achieves up to a 98% reduction in misalignment across various imitation learning algorithms. Furthermore, FMR exhibits robustness, performing effectively even when learning from limited, scarce aligned, or uninformative noisy demonstration data.

Key takeaway

For Machine Learning Engineers developing offline imitation learning agents, you should consider integrating Feedback Manipulation Regularization (FMR) to significantly improve policy alignment. This method offers up to a 98% reduction in misalignment and maintains robustness even when your training data is scarce or noisy. Implementing FMR can streamline your alignment process by moving from multi-stage pipelines to a more efficient single-stage approach for sequential decision-making.

Key insights

FMR uses evaluative feedback as a corrective signal to significantly improve offline imitation learning alignment, even with limited data.

Principles

• Evaluative feedback corrects imitation learning.
• Single-stage training improves sequential decision-making.
• Robustness in limited, noisy data regimes.

Method

Feedback Manipulation Regularization (FMR) integrates evaluative feedback as a corrective signal directly into single-stage offline training for fully sequential decision-making environments, enhancing policy alignment.

In practice

• Apply FMR for offline policy alignment.
• Use FMR with scarce or noisy demonstrations.
• Adapt Safety Gymnasium for alignment tests.

Topics

• Imitation Learning
• Agent Alignment
• Offline Reinforcement Learning
• Feedback Manipulation Regularization
• Safety Gymnasium
• Sequential Decision-Making

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

Related on AIssential

• Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning — Feedback Manipulation Regularization (FMR) uses evaluative feedback as a corrective signal for single-stage offline agent alignment in imitation learning.
• Difference-Aware Retrieval Policies for Imitation Learning — DARP enhances imitation learning generalization by leveraging local neighborhood structure and retrieval from expert demonstrations.
• Reflex: Reinforcement Learning with Reflection Symmetry Exploitation in State-Based Continuous Control — Exploiting reflection symmetry in state-based RL significantly improves sample efficiency and performance.
• Rethinking imitation learning with Predictive Inverse Dynamics Models — PIDMs improve imitation learning by predicting future states, reducing ambiguity and data requirements compared to Behavior Cloning.
• RLHF: Aligning Language Models with Human Feedback — RLHF aligns language models by leveraging human judgments, recognizing that judging quality is easier than producing it.
• ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning — ASALT enables MARL transfer learning across domains with mismatched state-space dimensionalities using adaptive embedding.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.