Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning
arXiv:2607.07859v1 Announce Type: cross Abstract: Reinforcement learning (RL) research has increasingly shifted focus towards alignment, ensuring agents learn behaviors adhering to human values. While human demonstrations and feedback have proven crucial for alignment, existing approaches predominantly combine these signals using multi-stage pipelines designed for the contextual bandit framing of language generation. Yet little work explores how these complementary inputs can serve as a richer, interconnected signal for single-stage offline training in fully sequential decision-making environm
The increasing focus on AI alignment and the limitations of current multi-stage feedback approaches in reinforcement learning necessitate new methodologies for integrating human guidance effectively.
Improving offline agent alignment through better integration of human feedback and demonstrations is critical for developing more reliable and ethically sound AI systems, particularly autonomous agents.
This research explores a single-stage, offline training approach for incorporating human feedback in fully sequential decision-making, moving beyond traditional language generation contexts.
- · AI safety researchers
- · Developers of autonomous AI agents
- · Companies implementing advanced AI systems
- · AI systems prone to misalignment
- · Current multi-stage feedback paradigms
More robust and aligned AI agents emerge from improved training methodologies.
Increased public and institutional trust in the deployment of autonomous AI systems across various sectors.
Acceleration of AI adoption in sensitive areas where human values and safety are paramount, potentially leading to new regulatory frameworks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG