10 papers
IAPO: Input Attribution-Aware Policy Optimization for Tool Use in Small Multimodal Agents
Yifan Yang, Zhen Zhang, Jiayi Tian +2
This paper investigates reinforcement learning (RL) methods for improving tool-calling capabilities in multimodal small language model (SLM) agents. While existing works have explo…
LoRi: Low-Rank Distillation for Implicit Reasoning
Ryan Solgi, Jiayi Tian, Zheng Zhang
Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting. We empirically find that hidden-state…
RankGuide: Tensor-Rank-Guided Routing and Steering for Efficient Reasoning
Jiayi Tian, Yupeng Su, Ryan Solgi +2
Large reasoning models (LRMs) enhance problem-solving capabilities by generating explicit multi-step chains of thought (CoT) reasoning; however, they incur substantial inference la…
Tensor-Compressed and Fully-Quantized Training of Neural PDE Solvers
Jinming Lu, Jiayi Tian, Yequan Zhao +2
Physics-Informed Neural Networks (PINNs) have emerged as a promising paradigm for solving partial differential equations (PDEs) by embedding physical laws into neural network train…
SkipKV: Selective Skipping of KV Generation and Storage for Efficient Inference with Large Reasoning Models
Jiayi Tian, Seyedarmin Azizi, Yequan Zhao +7
Large reasoning models (LRMs) often incur significant key-value (KV) cache overhead, due to their linear growth with the verbose chain-of-thought (CoT) reasoning. This incurs both…
Comprehensive Design Space Exploration for Tensorized Neural Network Hardware Accelerators
Jinsong Zhang, Minghe Li, Jiayi Tian +2
High-order tensor decomposition has been widely adopted to obtain compact deep neural networks for edge deployment. However, existing studies focus primarily on its algorithmic adv…
Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM
Ryan Solgi, Parsa Madinei, Jiayi Tian +4
Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment.…
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression
Jiayi Tian, Ryan Solgi, Jinming Lu +3
Large Language Models (LLMs) have enabled remarkable progress in natural language processing, yet their high computational and memory demands pose challenges for deployment in reso…
FETTA: Flexible and Efficient Hardware Accelerator for Tensorized Neural Network Training
Jinming Lu, Jiayi Tian, Hai Li +2
The increasing demand for on-device training of deep neural networks (DNNs) aims to leverage personal data for high-performance applications while addressing privacy concerns and r…
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization
Jiayi Tian, Jinming Lu, Hai Li +4
Transformer models have achieved state-of-the-art performance across a wide range of machine learning tasks. There is growing interest in training transformers on resource-constrai…