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cs.LG2024
REFA: Reference Free Alignment for multi-preference optimization
Taneesh Gupta, Rahul Madhavan, Xuchao Zhang +2
To mitigate reward hacking from response verbosity, modern preference optimization methods are increasingly adopting length normalization (e.g., SimPO, ORPO, LN-DPO). While effecti…
cs.LG2024
Multi-Preference Optimization: Generalizing DPO via Set-Level Contrasts
Taneesh Gupta, Rahul Madhavan, Xuchao Zhang +3
Direct Preference Optimization (DPO) has become a popular approach for aligning language models using pairwise preferences. However, in practical post-training pipelines, on-policy…
cs.CL2024
CARMO: Dynamic Criteria Generation for Context-Aware Reward Modelling
Taneesh Gupta, Shivam Shandilya, Xuchao Zhang +5
Reward modeling in large language models is susceptible to reward hacking, causing models to latch onto superficial features such as the tendency to generate lists or unnecessarily…