Yes, the stabilization techniques introduced in FLaRe could be effectively applied to other reinforcement learning (RL) fine-tuning settings, including fine-tuning language models and vision-language models. The core principles behind these techniques—such as using smaller learning rates, disabling entropy bonuses, and separating actor and critic networks—are broadly applicable across various domains where RL is utilized.
For instance, in fine-tuning language models, using a smaller learning rate can help prevent catastrophic forgetting, where the model loses previously learned information during the fine-tuning process. This is particularly important when adapting language models to specific tasks or domains, as it allows the model to retain its general language understanding while learning task-specific nuances.
Similarly, the separation of actor and critic networks can enhance stability in vision-language models, where the complexity of the input data can lead to unstable training dynamics. By ensuring that the feature extraction processes for the actor and critic are independent, the model can maintain robust representations while optimizing for specific tasks.
Furthermore, the use of on-policy algorithms, as demonstrated in FLaRe, can improve sample efficiency in these settings by ensuring that the model learns from the most relevant and recent experiences. This is crucial in language and vision-language tasks, where the context and relevance of data can change rapidly.
Overall, the stabilization techniques from FLaRe offer valuable insights that can enhance the performance and sample efficiency of RL fine-tuning across various applications, including language and vision-language models.