Decision Transformer
PulseAugur coverage of Decision Transformer — every cluster mentioning Decision Transformer across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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New Trust Guided Decision Transformer improves AI long-term decision-making
Researchers have developed a new method called Trust Guided Decision Transformer (TGDT) to improve the performance of Decision Transformers in long-term AI decision-making. Standard Decision Transformers struggle when t…
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New Decision Transformer Optimizes UAV Fleet Scheduling for Energy and Delay
Researchers have developed PrefDT, a novel preference-conditioned Decision Transformer designed for multi-objective scheduling in unmanned aerial vehicle (UAV) fleets operating in mobile edge computing (MEC) environment…
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Decision Transformer optimizes UAV-RIS assisted D2D communications
Researchers have developed a Decision Transformer model to optimize dynamic device-to-device (D2D) communications assisted by reconfigurable intelligent surfaces (RIS) mounted on unmanned aerial vehicles (UAVs). This ap…
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New diagnostic method audits recommender system control knobs
Researchers have developed a new diagnostic method to audit the effectiveness of "return conditioning" in recommender systems that utilize Decision Transformers. This method tests how changes in return-to-go (RTG) token…
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New generative auto-bidding methods improve ad performance and efficiency
Two new research papers, QGA and PRO-Bid, introduce advanced methods for generative auto-bidding in e-commerce advertising. QGA utilizes a Q-value regularization with a Decision Transformer backbone to optimize both pol…
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New framework TAF improves decision-sequence learning with misaligned data
Researchers have developed a new framework called Target-Aligned Fusion (TAF) to improve decision-sequence learning when using external data that may not perfectly align with the target environment. TAF addresses dynami…
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New HOBA framework enhances online advertising bidding with hierarchical RL
Researchers have developed HOBA, a novel hierarchical reinforcement learning framework designed to improve online advertising bidding systems. This system decouples strategic reasoning, model selection, and bid executio…
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New SOV-CAD framework reconstructs CAD sequences using stepwise visual feedback
Researchers have developed SOV-CAD, a new framework for reconstructing Computer-Aided Design (CAD) modeling sequences from images. Unlike previous methods that generate entire sequences at once, SOV-CAD mimics human des…
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New GLAN framework enhances personalized landing page recommendations
Researchers have developed GLAN, a novel sequence modeling framework designed to improve personalized landing page recommendations on online platforms. GLAN addresses limitations in previous reinforcement learning appro…
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OnDeFog enhances reinforcement learning for frame-dropping environments
Researchers have introduced OnDeFog, an advancement in reinforcement learning designed to handle frame dropping, a common issue in real-world applications due to communication delays or sensor failures. This new method …
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SlimDT paper proposes injecting RTG outside sequential modeling
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 injectin…
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QHyer model enhances offline goal-conditioned RL with adaptive history compression
Researchers have developed QHyer, a novel approach for offline goal-conditioned reinforcement learning that addresses challenges posed by partially observable and history-dependent datasets. QHyer utilizes a Q-estimator…
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Gemma 4 31B weights show cross-modal transfer via thin trainable interface
Researchers have demonstrated that frozen weights from the Gemma 4 31B text-pretrained model can be effectively reused across different modalities, including robotics and associative recall tasks. By employing a thin, t…