New Paper Uses Inverse RL to Extract Auditable Alignment Rewards from Demos
nagpalchirag · x · 2026-07-29
- The paper argues that current alignment methods underuse expert demonstrations because SFT imitates tokens directly, while RLHF/DPO need expensive preference pairs or verifiers.
- It proposes PARED (Projected Alignment Reward Estimated from Demonstrations), which recovers an explicit reward from demonstrations using a lightweight discriminator over a small set of response-level features.
- The reward is designed to be inspectable, reusable, and auditable, and it does not require task-specific preference annotations.
- The authors report gains from both inference-time reranking and adversarial on-policy RL, and say the method can also support contextual alignment by tailoring one policy to different audiences.
- The feature space can include simple human-interpretable signals such as response length or stylistic traits, and can be extended with AI feedback.
Related event: New PARED Method Extracts Auditable Alignment Rewards from Demonstrations(3 posts)→
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