Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Optimización Directa de Preferencias× | Mamba (modelo de espacio de estados)× | |
|---|---|---|
| Campo | Aprendizaje profundo | Aprendizaje profundo |
| Familia | Machine learning | Machine learning |
| Año de origen | 2023 | 2023 |
| Autor original≠ | Rafael Rafailov | Albert Gu |
| Tipo≠ | Training methodology | Neural network architecture |
| Fuente seminal≠ | 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. link ↗ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ |
| Alias≠ | DPO, Direct preference | Mamba, State space models, Selective state space |
| Relacionados | 4 | 4 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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