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cs.LG2026
Distributionally Robust Token Optimization in RLHF
Yeping Jin, Jiaming Hu, Ioannis Ch. Paschalidis
Large Language Models (LLMs) tend to respond correctly to prompts that align well with the data they were trained and fine-tuned on. Yet, small shifts in wording, format, or langua…
cs.LG2026
Towards General Preference Alignment: Diffusion Models at Nash Equilibrium
Jiaming Hu, Jiamu Bai, Haoyu Wang +2
Reinforcement learning from human feedback (RLHF) has been popular for aligning text-to-image (T2I) diffusion models with human preferences. As a mainstream branch of RLHF, Direct…