概念导读
Multi-domain on-policy distillation extends the OPD framework to handle multiple task domains simultaneously, training a single student model that can perform across diverse domains by distilling from domain-specific or generalist teachers. This concept is related to cascade-rl, which uses cascaded reinforcement learning for multi-domain post-training.
- Related
- References
Multi-Domain On-Policy Distillation
Multi-domain on-policy distillation extends the OPD framework to handle multiple task domains simultaneously, training a single student model that can perform across diverse domains by distilling from domain-specific or generalist teachers. This concept is related to cascade-rl, which uses cascaded reinforcement learning for multi-domain post-training.
Related
- cascade-rl — Cascade RL addresses multi-domain post-training with cascaded RL
- on-policy-distillation — Core OPD concept
- multi-task-learning — Multi-domain OPD is a form of multi-task learning