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cs.LG2026
Automating Potential-based Reward Shaping with Vision Language Model Guidance
Henrik Müller, Daniel Kudenko
Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly attribute the sparse success re…
cs.CR2026
Towards Sensitivity-Aware Language Models
Dren Fazlija, Iyiola E. Olatunji, Daniel Kudenko +1
With LLMs increasingly deployed in corporate data management, it is crucial to ensure that these models do not leak sensitive information. In the context of corporate data manageme…