Human-inspired, Task-Dimension-Guided Exploration for Efficient Learning in High Dimensions
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Efficient exploration in high-dimensional decision spaces remains a central challenge for decision-making systems. Humans, in contrast, can navigate large decision spaces with remarkable efficiency. R...
arXiv:2609.36473v1 Announce Type: new Abstract: Temporal abstraction via options can improve exploration in large environments. However, existing option discovery algorithms find subgoals that target...
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arXiv:2609.15094v1 Announce Type: cross Abstract: In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user pop...
SteerGR is a framework that enables concept-level control in generative recommendation for game discovery. It uses sparse autoencoders to define play concepts with an LLM, discover and select features associated with those concepts, and then intervene on the selected features to steer recommendations without retraining the model. Experiments on two industrial generative recommendation models demonstrate that the selected features can successfully steer recommendations toward or away from target concepts.
arXiv:2504. 17356v3 Announce Type: replace Abstract: Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task.