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 reinforcement learning, we introduce Projected Alignment Reward Estimated from Demonstrations (PARED). PARED recovers the implicit reward underlying expert demonstrations as an explicit function ov...
| Authors | Michał Wiliński, Liu Leqi, Chirag Nagpal |
|---|---|
| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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