19 citations · 26 across the 11 of their papers we have counts for
11 papers
ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients
Yixiao Li, Tifanny Portela, Jordis Herrmann +2
Neural Motion Planners (NMPs) enable fast reactive motion generation, but adapting them to new environments typically requires recollecting large expert datasets, which is computat…
Controllable and Verifiable Tool-Use Data Synthesis for Agentic Reinforcement Learning
Siyuan Xu, Shiyang Li, Xin Liu +9
Existing synthetic tool-use corpora are primarily designed for offline supervised fine-tuning, yet reinforcement learning (RL) requires executable environments that support reward-…
Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Prediction
Yining Qian, Pengjie Wang, Yixiao Li +4
Predicting drug-target affinity is fundamental to virtual screening and lead optimization. However, existing deep models often suffer from representation collapse in stringent cold…
EBGAN-MDN: An Energy-Based Adversarial Framework for Multi-Modal Behavior Cloning
Yixiao Li, Julia Barth, Thomas Kiefer +1
Multi-modal behavior cloning faces significant challenges due to mode averaging and mode collapse, where traditional models fail to capture diverse input-output mappings. This prob…
Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
Ajay Jaiswal, Jianyu Wang, Yixiao Li +6
Sparsely activated Mixture-of-Experts (SMoE) has shown promise in scaling up the learning capacity of neural networks. However, vanilla SMoEs have issues such as expert redundancy…
NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models
Lawrence Liu, Inesh Chakrabarti, Yixiao Li +3
Large language models (LLMs) exhibit remarkable performance across various natural language processing tasks but suffer from immense computational and memory demands, limiting thei…
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
Yixiao Li, Xianzhi Du, Ajay Jaiswal +4
Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown…
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
Ilgee Hong, Zichong Li, Alexander Bukharin +4
Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values by learning rewards from human preference data. Due to various reaso…
DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking
Yichuan Li, Junkai Zhao, Yixiao Li +4
Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demandin…
LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models
Yixiao Li, Yifan Yu, Chen Liang +4
Quantization is an indispensable technique for serving Large Language Models (LLMs) and has recently found its way into LoRA fine-tuning. In this work we focus on the scenario wher…
Deep Reinforcement Learning from Hierarchical Preference Design
Alexander Bukharin, Yixiao Li, Pengcheng He +1
Reward design is a fundamental, yet challenging aspect of reinforcement learning (RL). Researchers typically utilize feedback signals from the environment to handcraft a reward fun…