ArXiv TLDR

Zhiyuan Liu

3 papers · Latest:

Machine Learning

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices

DECO is a sparse MoE model matching dense performance on end-side devices, offering 3x speedup and reduced storage overhead.

2605.10933
Robotics

MISTY: High-Throughput Motion Planning via Mixer-based Single-step Drifting

MISTY is a high-throughput, single-step motion planner using a mixer-based architecture, achieving state-of-the-art performance and significant speedup.

2604.21489
Machine Learning

Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe

This paper investigates on-policy distillation (OPD) dynamics in LLMs, identifying success conditions, token-level mechanisms, and practical recovery strategies.

2604.13016

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