Derived from arXiv:2607.17760 - Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
Scanned 9/11/2026
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# Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
Derived from arXiv:2607.17760 - Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning
## Core Concept
Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations. However, real-world tasks often exhibit substantial natural variations (e.g., picking up mugs with varying shapes), making it impractical to collect demonstrations that fully specify a new task under every possible scenario. In practice, while demonstrations for the target task are limited, it is often easier to obtain datasets of heterogeneous but related behaviors. This motivates the problem of...
## Key Insights
- Derived from arXiv:2607.17760
- Published: 2026-07-20
- Utility Score: 1.00
- Authors: Ziyi Liu, Grace Zhang
## Activation
generalize-and-guide-decomposing-rewards-for-few-s, 2607.17760
## References
- arXiv: https://arxiv.org/abs/2607.17760
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