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cs.LG2026
Bellman Meets Lyapunov: Unsupervised Reinforcement Learning via Mastering Chaos
Tristan Shah, Wooyoung Chung, Volodomyr Makarenko +2
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…
cs.LG2026
Beyond Pairwise Feedback: Listwise Vision-Language Supervision for Preference-Based Reward Learning
Srivalli Katkuri, Maxwell Kawada, Juan Wachs
Vision-language models (VLMs) have emerged as a powerful source of supervision for reinforcement learning, enabling agents to leverage rich semantic knowledge during training. Insp…