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Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design
Fu Feng, Ruixiao Shi, Yucheng Xie +2
Morphology--control co-design jointly optimizes an agent's body structure and control policy as an integrated embodied system. However, existing methods typically model morphology…
Constraint-based Pre-training: From Structured Constraints to Scalable Model Initialization
Fu Feng, Yucheng Xie, Ruixiao Shi +2
The pre-training and fine-tuning paradigm has become the dominant approach for model adaptation. However, conventional pre-training typically yields models at a fixed scale, wherea…
A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling
Jianlu Shen, Fu Feng, Jiaze Xu +3
Transferring pre-trained knowledge from a source model to a target model of a different architectural size is a key challenge for flexible and efficient model scaling. However, cur…
Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
Jianlu Shen, Fu Feng, Yucheng Xie +2
Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coup…
Knowledge Diversion for Efficient Morphology Control and Policy Transfer
Fu Feng, Ruixiao Shi, Yucheng Xie +3
Universal morphology control aims to learn a universal policy that generalizes across heterogeneous agent morphologies, with Transformer-based controllers emerging as a popular cho…
WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models
Fu Feng, Yucheng Xie, Jing Wang +1
The growing complexity of model parameters underscores the significance of pre-trained models. However, deployment constraints often necessitate models of varying sizes, exposing l…
Transferring Core Knowledge via Learngenes
Fu Feng, Jing Wang, Xin Geng
The pre-training paradigm fine-tunes the models trained on large-scale datasets to downstream tasks with enhanced performance. It transfers all knowledge to downstream tasks withou…