Andi Nika et al. analyze the susceptibility of two preference-based learning paradigms to poisoned data: reinforcement learning from human feedback (RLHF), which learns a reward model using preferences, and direct preference optimization (DPO), which directly optimizes a policy using preferences.

Authors

• Person: Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche Organization: AISTATS
  Track: Poster Session 3 - aistats 2026

Sources

• Track: Poster Session 3 - aistats 2026 virtual.aistats.org Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche · AISTATS via serper

Referenced by nodes (2)

• Reinforcement learning from human feedback (RLHF) concept
• Direct Preference Optimization (DPO) concept