Back to feed
arXiv cs.LG
arXiv cs.LG
7/29/2026
Inverse RL Helps Align AI by Imitating Humans

Inverse RL Helps Align AI by Imitating Humans

Short summary

PARED recovers an implicit reward function from expert demonstrations using a lightweight discriminator, eliminating the need for task-specific preference annotations. The recovered reward improves base policies through inference-time reranking and adversarial on-policy RL, and can be optimized after standard SFT for further gains. PARED also supports contextual alignment, tailoring a single policy to different audience preferences.

  • •PARED extracts implicit rewards from demonstrations without preference annotations via a feature-space discriminator
  • •Recovered reward improves policies through reranking and on-policy RL, with gains after SFT
  • •Supports contextual alignment—single policy adapted to different audience preferences

Generated with AI, which can make mistakes.

#research-breakthrough
Read full article at arXiv cs.LG

Is this a good recommendation for you?

Comments

Failed to load comments. Please try again.

Explore more

Inference-Time Policy Alignment for Fair Reinforcement Learning
arXiv cs.LG
Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback
arXiv cs.LG
RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences
arXiv cs.LG
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
arXiv CS.AI