# Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer Source: [Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer]() Collection index: [llms.txt]() Data updated: 2026-05-09T05:00:12.752Z Authors: Yongyi Wang, Hanyu Liu, Lingfeng Li, Bozhou Chen, Ang Li, Qirui Zheng, Xionghui Yang, Chucai Wang, Wenxin Li Published: 2026-05-07T12:20:25.000Z ## Abstract Decision Transformer (DT) formulates offline reinforcement learning as autoregressive sequence modeling, achieving promising results by predicting actions from a sequence of Return-to-Go (RTG), state, and action tokens. However, RTG is a scalar that summarizes future rewards, containing far less information than typical state or action vectors, yet it consumes the same computational budget per token. Worse, the self-attention cost of Transformers grows quadratically with sequence length, so including RTG as a separate token adds unnecessary overhead. We propose SlimDT, which removes RTG from the autoregressive sequence. Instead, we inject RTG information into the state representations before the sequential modeling step, allowing the Transformer to process only a compact (state, action) sequence. This reduces the sequence length by one-third, directly improving inference efficiency. On the D4RL benchmark, SlimDT surpasses standard DT across various tasks and achieves performance comparable to existing state-of-the-art methods. Decoupling a sparse conditioning signal from an information-rich sequence thus yields both computational gains and higher task performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Dexterous Manipulation on D4RL Adroit human cloned v2](): Dexterous Manipulation; D4RL Adroit human cloned v2; Performance (Human Expert): 94.4. - [Locomotion on D4RL MuJoCo Tasks (Average across v2 Environments)](): Locomotion; D4RL MuJoCo Tasks; Average D4RL Locomotion Score (v2): 82.6. ## Original sources and implementation [Original paper]() [PDF]()