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cs.LG2026
To Go Far, Go Together: Diverse Preferences Induce a Curriculum for Reward Optimization
Taehyung Kim, Jongeun Choi
Learning a reward model from human feedback and optimizing a policy against it is one approach to aligning AI systems with individual users. From a fairness perspective, existing w…
cs.LG2025
Symmetry-Aware Steering of Equivariant Diffusion Policies: Benefits and Limits
Minwoo Park, Junwoo Chang, Jongeun Choi +1
Equivariant diffusion policies (EDPs) combine the generative expressivity of diffusion models with the strong generalization and sample efficiency afforded by geometric symmetries.…
cs.LG2025
Partially Equivariant Reinforcement Learning in Symmetry-Breaking Environments
Junwoo Chang, Minwoo Park, Joohwan Seo +3
Group symmetries provide a powerful inductive bias for reinforcement learning (RL), enabling efficient generalization across symmetric states and actions via group-invariant Markov…