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cs.LG2026
DaCe-DT: Data-Centric Offline Multi-Task Reinforcement Learning via Adaptive Prompts and Trajectory Correction for Heterogeneous Tasks
Xinfei Wang, Shanchen Pang, Chenhao Zhang +5
Offline multi-task reinforcement learning (Offline MTRL) heavily depends on the quality and distribution of pre-collected data. However, existing methods mainly focus on algorithmi…
cs.LG2026
ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference
Xiongwei Zhu, Xiaojian Liao, Tianyang Jiang +3
Fine-grained Mixture-of-Experts (MoE) models sparsely activate only a subset of experts per token, reducing activated computation while maintaining high model capacity. However, in…
cs.LG2025
Soft Conflict-Resolution Decision Transformer for Offline Multi-Task Reinforcement Learning
Shudong Wang, Xinfei Wang, Chenhao Zhang +4
Multi-task reinforcement learning (MTRL) seeks to learn a unified policy for diverse tasks, but often suffers from gradient conflicts across tasks. Existing masking-based methods a…