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SlimDT paper proposes injecting RTG outside sequential modeling

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SlimDT, a modification of the Decision Transformer (DT) model for offline reinforcement learning. SlimDT removes the Return-to-Go (RTG) token from the autoregressive sequence, instead injecting this information directly into the state representations. This approach reduces the sequence length by one-third, leading to improved inference efficiency and computational gains. Experiments on the D4RL benchmark show SlimDT outperforming standard DT and achieving performance comparable to state-of-the-art methods. AI

IMPACT Introduces a more efficient variant of Decision Transformer, potentially improving performance and reducing computational costs in offline reinforcement learning tasks.

RANK_REASON This is a research paper detailing a novel modification to an existing model architecture for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SlimDT paper proposes injecting RTG outside sequential modeling

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This is a research paper detailing a novel modification to an existing model architecture for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yongyi Wang, Hanyu Liu, Lingfeng Li, Bozhou Chen, Ang Li, Qirui Zheng, Xionghui Yang, Chucai Wang, Wenxin Li ·

    Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer

    arXiv:2605.06104v1 Announce Type: new 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…

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