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Yongqiang Yao

7 accepted papers

2025

Hierachical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM

NeurIPS 2025poster

Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to consider imbalanced attention computation and wasted communic…

Cited by 0SourcecodeScholar
2025

OmniBal: Towards Fast Instruction-Tuning for Vision-Language Models via Omniverse Computation Balance

ICML 2025poster

Vision-language instruction-tuning models have recently achieved significant performance improvements. In this work, we discover that large-scale 3D parallel training on those models leads to an imbalanced computation load across different devices. The vision and language parts are inherently hetero…

2025

Tool Playgrounds: A Comprehensive and Analyzable Benchmark for LLM Tool Invocation

ICASSP 2025accepted

The rapid advancement of large language models (LLMs) has paved the way for their use in solving real-world problems, which in turn has significantly driven the development of tool-assisted LLMs. This progress necessitates thorough evaluation methods. However, existing benchmarks typically only prov…

Cited by 0SourceScholar
2023

Program Translation via Code Distillation

EMNLP 2023long main

Software version migration and program translation are an important and costly part of the lifecycle of large codebases. Traditional machine translation relies on parallel corpora for supervised translation, which is not feasible for program translation due to a dearth of aligned data. Recent unsupe…

Cited by 0SourceScholar
2023

SUT: Active Defects Probing for Transcompiler Models

EMNLP 2023short main

Automatic Program translation has enormous application value and hence has been attracting significant interest from AI researchers. However, we observe that current program translation models still make elementary syntax errors, particularly, when the target language does not have syntax elements i…

Cited by 0SourceScholar
2022

Equalized Focal Loss for Dense Long-Tailed Object Detection

CVPR 2022poster

Despite the recent success of long-tailed object detection, almost all long-tailed object detectors are developed based on the two-stage paradigm. In practice, one-stage detectors are more prevalent in the industry because they have a simple and fast pipeline that is easy to deploy. However, in the…

Cited by 142PDFcodeScholar
2020

Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection

CVPR 2020oral

Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually h…

Cited by 2298PDFcodeScholar