SPPO: Next-Level Language Model Alignment
Prepare to be amazed by the groundbreaking advancements of Self-Play Preference Optimization (SPPO) in the realm of language model alignment. Outshining its predecessors, DPO and IPO, SPPO's performance on AlpacaEval, MT-Bench, and the Open LLM Leaderboard is nothing short of astonishing. Serving as the evolutionary successor to "Self-Play Fine-tuning," SPPO introduces a novel loss function and harnesses the power of iterative training. Its methodology, outlined in a comprehensive step-by-step guide, involves the meticulous preparation of a reward model and a language model, generating multiple responses for input prompts, scoring these responses, and fine-tuning the language model based on estimated preference scores. Notably, SPPO Iter3 achieves remarkable scores on MT-Bench, showcasing its consistent improvement over baseline models. Furthermore, SPPO maintains overall performance on various metrics and is readily accessible through Hugging Face TRL main. Get ready to witness the future of language model alignment with SPPO.