Xwin-LM: Strong and Scalable Alignment Practice for LLMs
<|reference_start|>Xwin-LM: Strong and Scalable Alignment Practice for LLMs: In this work, we present Xwin-LM, a comprehensive suite of alignment methodologies for large language models (LLMs). This suite encompasses several key techniques, including supervised finetuning (SFT), reward modeling (RM), rejection sampling finetuning (RS), and direct preference optimization (DPO). The key components are as follows: (1) Xwin-LM-SFT, models initially finetuned with high-quality instruction data; (2) Xwin-Pair, a large-scale, multi-turn preference dataset meticulously annotated using GPT-4; (3) Xwin-RM, reward models trained on Xwin-Pair, developed at scales of 7B, 13B, and 70B parameters; (4) Xwin-Set, a multiwise preference dataset in which each prompt is linked to 64 unique responses generated by Xwin-LM-SFT and scored by Xwin-RM; (5) Xwin-LM-RS, models finetuned with the highest-scoring responses from Xwin-Set; (6) Xwin-LM-DPO, models further optimized on Xwin-Set using the DPO algorithm. Our evaluations on AlpacaEval and MT-bench demonstrate consistent and significant improvements across the pipeline, demonstrating the strength and scalability of Xwin-LM. The repository https://github.com/Xwin-LM/Xwin-LM will be continually updated to foster community research.<|reference_end|>
@article{ni2024xwin-lm:,
    title={Xwin-LM: Strong and Scalable Alignment Practice for LLMs},
    author={Bolin Ni, JingCheng Hu, Yixuan Wei, Houwen Peng, Zheng Zhang, Gaofeng
  Meng, Han Hu},
    journal={arXiv preprint arXiv:2405.20335},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20335},
    primaryClass={cs.CL}
}