direct-alignment

This paper introduces a regularization technique for Direct Alignment Algorithms (DAAs) that maintains normalized response probabilities, mitigating over-optimization and likelihood displacement. The method improves generation quality and benchmark performance, achieving over 20% relative increase on AlpacaEval2 and 9% gains on general benchmarks for Llama-3.1-8B-Instruct.