Weak-to-Strong Generalization via Direct On-Policy Distillation
Researchers propose a method to improve language model reasoning by distilling knowledge from a smaller model to a stronger one, reducing training costs. This approach enables the reuse of learned knowledge from a smaller model.
- Researchers propose a weak-to-strong distillation method for efficient model training
- This approach enables the reuse of learned knowledge from a smaller model
- The method aims to reduce training costs associated with reinforcement learning
- Direct on-policy distillation is used to transfer knowledge from the weak teacher to the stronger target model
The study focuses on reinforcement learning with verifiable rewards, a technique that enhances language model reasoning. However, this process can be expensive and time-consuming, especially when dealing with large models.
To address this issue, the researchers suggest an alternative approach: running reinforcement learning on a smaller model, where rollouts are cheaper, and then reusing the learned knowledge to improve a stronger target model.
The key challenge lies in effectively distilling the knowledge from the smaller model, known as the weak teacher, to the stronger target model. Simply distilling the post-RL weak teacher is not sufficient, as its final policy combines useful RL gains with limitations.
The proposed method aims to overcome this limitation by directly distilling the on-policy experience, allowing for more efficient and effective knowledge transfer between models.
Source: Weak-to-Strong Generalization via Direct On-Policy Distillation. Read the full piece at the source.
helps reduce training costs and improve model efficiency
contributes to the development of more efficient and effective language models
- reinforcement learning with verifiable rewards
- a technique that enhances language model reasoning by providing rewards for correct actions
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