REFA: Reference Free Alignment for multi-preference optimization

Abstract: We introduce $\textbf{REFA}$, a family of reference-free alignment methods that optimize over multiple user preferences while enforcing fine-grained length control. Our approach integrates deviation-based weighting to emphasize high-quality responses, length normalization to prevent trivial short-response solutions, and an EOS-probability regularizer to mitigate dataset-induced brevity biases. Theoretically, we show that under the Uncertainty Reduction with Sequence Length Assertion (URSLA) framework, naive length normalization can still incentivize length-based shortcuts. In contrast, REFA corrects these subtle incentives, guiding models toward genuinely more informative and higher-quality outputs. Empirically, REFA achieves a new $\textbf{state-of-the-art}$ among reference-free alignment methods, generating richer responses that align more closely with human preferences. Notably, REFA improves performance on the AlpacaEval2 benchmark, achieving a $\textbf{26.6%}$ Length-Controlled Win Rate (LC-WR) and $\textbf{24.2%}$ Win Rate (WR).