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This paper investigates how to improve the adaptation of diffusion models in a way that doesn't rely on a classifier, and how to address a problem where the model can't accurately learn from its teacher. Practitioners might care about this because it could lead to more effective knowledge transfer in machine learning applications.
This paper proposes a new method for training large language models in open-ended domains, using evolving contexts as in-training supervision to capture task preferences. Practitioners may care about this approach because it can lead to better performance on open-ended tasks.
This paper addresses a problem in on-policy distillation where a student model can get stuck on a wrong path, and proposes a new method called Relay-OPD that helps the student model recover by briefly taking over at certain points to produce a new trajectory. Practitioners might care about this because it could lead to better performance and more efficient training in models like language generators or math solvers.
This paper proposes a method to combine reinforcement learning with verifiable rewards and on-policy distillation to improve performance on complex tasks, and shows that this method can lead to more stable training and better results.
This paper proposes a method to improve on-policy distillation by reusing pre-collected teacher data, allowing for faster training without interacting with the environment. Practitioners may care about this technique because it enables scalable and efficient distillation of complex agent models.