পদ্ধতির তুলনা করুন

	প্রত্যক্ষ পছন্দ অপ্টিমাইজেশন	মাম্বা (স্টেট স্পেস মডেল)
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2023	2023
প্রবর্তক	Rafael Rafailov	Albert Gu
ধরন	Training methodology	Neural network architecture
মৌলিক উৎস	Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C. (2023). Direct preference optimization: Your language model is secretly a reward model. arXiv preprint arXiv:2305.18290.	Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956.
অপর নাম	DPO, Direct preference	Mamba, State space models, Selective state space
সম্পর্কিত	4	4
সারসংক্ষেপ	Direct Preference Optimization (DPO) is a training method introduced by Rafailov et al. in 2023 that aligns language models with human preferences without requiring an explicit reward model. By directly optimizing for preference pairs (better response vs worse response), DPO simplifies the training pipeline compared to reinforcement learning from human feedback (RLHF).	Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power.