Inverse RL Helps Align AI by Imitating Humans
Language model alignment aims to make model behavior reliably reflect desirable properties such as helpfulness, safety, and instruction following. Current approaches typically use supervised fine-tuning on demonstrations or reinforcement learning with rewards derived from verifiers or human feedback. These paradigms leave an important question underexplored: can demonstrations alone yield an implicit reward that can be inspected, reused, and optimized on-policy to align AI? Motivated by inverse
Record details
Published: 27 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Regulation · Safety & alignment
Retrieved: 29 July 2026
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ethics.ai (27 July 2026), “Inverse RL Helps Align AI by Imitating Humans,” evidence record 14484, https://ethics.ai/record/14484 (originally published by arXiv cs.LG).
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