Author name cluster

Xinyi He

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization

Mingzhe Du
Anh Tuan Luu
Yue Liu
Yuhao Qing
Dong Huang
Xinyi He
Qian Liu
Zejun Ma

Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization~(GRPO). Experiments on our Venus dataset and the APPS benchmark show that SFT and DPO rapidly saturate in efficiency gains. In contrast, GRPO, using reinforcement learning (RL) with execution feedback, continuously optimizes code performance, significantly boosting both pass@1 (from 47% to 62%) and the likelihood of outperforming human submissions in efficiency (from 31% to 45%). Our work demonstrates effective test-time code efficiency improvement and critically reveals the power of RL in teaching LLMs to truly self-improve code efficiency. We released our code and data at https: //github. com/Elfsong/Afterburner.

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IROS Conference 2025 Conference Paper

Diffusion Suction Grasping with Large-Scale Parcel Dataset

Ding-Tao Huang
Debei Hua
Dongfang Yu
Xinyi He
En-Te Lin
Liang-Hong Wang
Jin-Liang Hou
Long Zeng 0001

While recent advances in suction grasping have shown remarkable progress, significant challenges persist particularly in cluttered and complex parcel handling scenarios. Current approaches are limited by (1) the lack of comprehensive parcel-specific suction grasp datasets and (2) poor adaptability to diverse object properties, including size, geometry, and texture. We address these challenges through two main contributions. Firstly, we introduce the Parcel-Suction-Dataset, a large-scale synthetic dataset containing 25 thousand cluttered scenes with 410 million precision-annotated suction grasp poses, generated via our novel geometric sampling algorithm. Secondly, we propose Diffusion-Suction, a framework that innovatively reformulates suction grasp prediction as a conditional generation task using denoising diffusion probabilistic models. Our method iteratively refines random noise into suction grasping score through visual-conditioned guidance from point cloud observations, effectively learning spatial point-wise affordances from our synthetic dataset. Extensive experiments demonstrate that the simple yet efficient Diffusion-Suction achieves new state-of-the-art performance compared to previous models on both Parcel-Suction-Dataset and the public SuctionNet-1Billion benchmark. This work provides a robust foundation for advancing automated parcel handling systems in real-world applications.

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AAAI Conference 2024 Conference Paper

Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries

Xinyi He
Mengyu Zhou
Xinrun Xu
Xiaojun Ma
Rui Ding
Lun Du
Yan Gao
Ran Jia

Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like forecasting and chart generation. To address this gap, we developed the Text2Analysis benchmark, incorporating advanced analysis tasks that go beyond the SQL-compatible operations and require more in-depth analysis. We also develop five innovative and effective annotation methods, harnessing the capabilities of large language models to enhance data quality and quantity. Additionally, we include unclear queries that resemble real-world user questions to test how well models can understand and tackle such challenges. Finally, we collect 2249 query-result pairs with 347 tables. We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.

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IJCAI Conference 2022 Conference Paper

Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks

Haoyu Dong
Zhoujun Cheng
Xinyi He
Mengyu Zhou
Anda Zhou
Fan Zhou
Ao Liu
Shi Han

Following the success of pre-training techniques in the natural language domain, a flurry of table pre-training frameworks have been proposed and have achieved new state-of-the-arts on various downstream tasks such as table question answering, table type recognition, column relation classification, table search, and formula prediction. Various model architectures have been explored to best capture the characteristics of (semi-)structured tables, especially specially-designed attention mechanisms. Moreover, to fully leverage the supervision signals in unlabeled tables, diverse pre-training objectives have been designed and evaluated, for example, denoising cell values, predicting numerical relationships, and learning a neural SQL executor. This survey aims to provide a comprehensive review of model designs, pre-training objectives, and downstream tasks for table pre-training, and we further share our thoughts on existing challenges and future opportunities.

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