Adaptive Policy Backbone: A Meta-Transfer RL Method for Out-of-Distribution Tasks
Researchers have introduced the Adaptive Policy Backbone (APB), a novel meta-transfer reinforcement learning technique. This method incorporates lightweight linear layers both before and after a common backbone network, facilitating efficient fine-tuning of parameters while maintaining existing knowledge. APB enhances sample efficiency compared to traditional RL and is capable of adapting to out-of-distribution tasks, where current meta-RL benchmarks often struggle. It tackles the issue of task discrepancies between training and deployment, which can undermine the effectiveness of prior knowledge, such as pre-existing datasets or reference policies. The paper can be found on arXiv with the identifier 2509.22310 and is categorized under Computer Science > Machine Learning.
Key facts
- APB is a meta-transfer RL method that inserts lightweight linear layers before and after a shared backbone.
- APB enables parameter-efficient fine-tuning (PEFT) while preserving prior knowledge during adaptation.
- APB improves sample efficiency over standard RL.
- APB adapts to out-of-distribution (OOD) tasks where existing meta-RL baselines typically fail.
- The method addresses task mismatch between training and deployment.
- The paper is available on arXiv with identifier 2509.22310.
- The paper is categorized under Computer Science > Machine Learning.
- The method leverages priors such as pre-collected datasets or reference policies.
Entities
Institutions
- arXiv