Reverse Preference Optimization for Complex Instruction Following
Authors: name: Xiang Huang, name: Ting-En Lin, name: Feiteng Fang, name: Yuchuan Wu, name: Hangyu Li, name: Yuzhong Qu, name: Fei Huang, name: Yongbin Li

Instruction following (IF) is a critical capability for large language models
(LLMs). However, handling complex instructions with multiple constraints
remains challenging. Previous methods typically select preference pairs based
on the number of constraints they satisfy, introducing noise where chosen
examples may fail to follow some constraints and rejected examples may excel in
certain respects over the chosen ones. To address the challenge of aligning
with multiple preferences, we propose a simple yet effective method called
Reverse Preference Optimization (RPO). It mitigates noise in preference pairs
by dynamically reversing the constraints within the instruction to ensure the
chosen response is perfect, alleviating the burden of extensive sampling and
filtering to collect perfect responses. Besides, reversal also enlarges the gap
between chosen and rejected responses, thereby clarifying the optimization
direction and making it more robust to noise. We evaluate RPO on two multi-turn
IF benchmarks, Sysbench and Multi-IF, demonstrating average improvements over
the DPO baseline of 4.6 and 2.5 points (on Llama-3.1 8B), respectively.
Moreover, RPO scales effectively across model sizes (8B to 70B parameters),
with the 70B RPO model surpassing GPT-4o.