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J. Wachs

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#machine learning Preprint Oct 2026

Bellman Meets Lyapunov: Unsupervised Reinforcement Learning via Mastering Chaos

Reinforcement learning (RL) is a powerful paradigm for training agents, yet its success rests on domain expertise of human engineers who design informative reward signals for every new task. Unsupervised RL aims to reduce this engineering with intrinsic motivation (IM): reward signals that emerge from the agent environ...

Tristan A. Shah, Wooyoung Chung, Volodomyr Makarenko et al. · 0 citations
Preprint Aug 2026

Beyond Pairwise Feedback: Listwise Vision-Language Supervision for Preference-Based Reward Learning

It is shown that Plackett-Luce (PL) reward models can train robotic policies from VLM-generated rankings as effectively as pairwise Bradley-Terry, $K$-wise Bradley-Terry, and RL-VLM-F baselines and demonstrate that listwise VLM preference supervision is a competitive and flexible approach to reward learning for reinfor...

Srivalli Katkuri, Maxwell Kawada, J. Wachs · 0 citations

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