Title: Online Finetuning Decision Transformers with Policy Gradients
Primary area: reinforcement learning
Keywords: reinforcement learning, decision transformer, continuous control

Abstract: Decision Transformer (DT) has emerged as a powerful paradigm for decision making by formulating offline Reinforcement Learning (RL) as a sequence modeling problem. While recent studies have started to investigate how Decision Transformers can be extended to online settings, online finetuning with pure RL gradients remains largely underexplored: most existing approaches continue to prioritize supervised sequence modeling losses during the online phase. In this paper, we introduce a new algorithm that performs online finetuning solely through generative rollouts and the corresponding RL gradients. Our approach represents a novel adaptation of the classical GRPO algorithm to the online finetuning of Decision Transformers. To make GRPO efficient and compatible with DTs, we incorporate several key modifications, including sub-trajectory sampling, sequence-likelihood objectives, and a reset-based sampling strategy. We conduct extensive experiments across diverse benchmarks and show that, on average, our method significantly outperforms existing online finetuning approaches such as ODT and ODT+TD3. This opens a new direction for advancing the online finetuning of Decision Transformers.

TL;DR: We adapted GRPO to fine-tune pretrained decision transformers with pure policy gradients, achieving state-of-the-art performance on several benchmarks.