Abstract: Language model alignment methods, such as reinforcement learning from human feedback (RLHF), haveled to impressive advances in language model capabilities. However, existing techniques are limited by a widely observed phenomenon known as *overoptimization*, where the quality of the language model degrades over the course of the alignment process. Overoptimization occurs when a language model overfits to inaccuracies in an (either explicit or implicit) offline reward model, and drifts away from preferred responses covered by the data. To discourage such distribution shift, offline alignment methods typically employ KL-regularization, but this, as we show, is too weak to prevent degradation in performance. Then, can we design an efficient algorithm that is provably robust to overoptimization?In this paper, we advance theoretical understanding of sample-efficient offline alignment and introduce a new algorithm called $\chi^2$-Preference Optimization ($\chi$PO). $\chi$PO is a one-line change to Direct Preference Optimization (DPO; Rafailov et al. 2023), that modifies only the logarithmic link function in the DPO objective. Despite this minimal change, $\chi$PO implicitly implements the principle of *pessimism in the face of uncertainty* via regularization with the $\chi^2$-divergence---which quantifies uncertainty more effectively than KL-regularization---and provably alleviates overoptimization, achieving sample-complexity guarantees based on *single-policy concentrability*---the gold standard in offline reinforcement learning. This guarantee makes $\chi$PO the first simple, yet general-purpose offline alignment algorithm that is provably robust to overoptimization.