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Xiaojian Liao

2 accepted papers

2026

ReMoE: Boosting Expert Reuse through Router Fine-Tuning in Memory-Constrained MoE LLM Inference

ICML 2026poster

Fine-grained Mixture-of-Experts (MoE) models sparsely activate a subset of parameters, significantly reducing computational costs while maintaining performance. However, in memory-constrained inference scenarios, only a small set of experts can be cached. Experts not in the cache must be fetched fro…

Cited by 0SourceScholar
2026

Soft Conflict-Resolution Decision Transformer for Offline Multi-Task Reinforcement Learning

AAAI 2026technical

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 attempt to mitigate such conflicts by assigning task-specific parameter masks. However, our empirical study shows that co

Cited by 0SourcePDFScholar