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Found 2 papers, 1 papers with code
Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step Optimization
However, while RL-free methods deliver satisfactory performance, they require significant data to develop a robust Supervised Fine-Tuned (SFT) model and an additional step to fine-tune this model on a preference dataset, which constrains their utility and scalability.
Insights into Alignment: Evaluating DPO and its Variants Across Multiple Tasks
Key observations reveal that alignment methods achieve optimal performance with smaller training data subsets, exhibit limited effectiveness in reasoning tasks yet significantly impact mathematical problem-solving, and employing an instruction-tuned model notably influences truthfulness.
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