Peijie Dong

Peijie Dong

PhD student in DSA, The Hong Kong University of Science and Technology (Guangzhou)

I am Peijie Dong (董佩杰), a final-year Ph.D. candidate in the Data Science and Analytics Thrust at the Hong Kong University of Science and Technology (Guangzhou), advised by Prof. Xiaowen Chu and Prof. Junxian He. I am currently a research intern with the WorkBuddy/CodeBuddy Coding Agent team at Tencent CSIG, where I work on post-training and evaluation for long-horizon coding agents.

Research Interests

My research focuses on improving the ability of coding agents to solve long-horizon, repository-level software engineering tasks. I am particularly interested in transforming interaction trajectories and environment feedback into effective training signals. My current research interests include:

  • Coding Agent Post-Training: Developing data and training recipes for coding agents, including trajectory curation, supervised fine-tuning, reinforcement learning, and reward design.
  • Long-Horizon Agent Evaluation: Building benchmarks and agent harnesses to study planning, tool use, repository navigation, error recovery, and end-to-end task completion.
  • Agent Data and Training-Evaluation Loops: Diagnosing behavioral failures from agent trajectories and translating them into targeted data, objectives, and evaluation signals.
  • Efficient Large Language Models: Improving the efficiency of LLM training and inference through model compression, low-precision training, efficient architectures, and systems optimization.

My long-term goal is to build coding agents that can learn from complete interaction trajectories and reliably improve through real-world task feedback. I welcome discussions and collaborations on coding agents, post-training, evaluation, and efficient LLMs.

🔥 News

  • [2026.07]  🎉🎉 Our tech report “Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction” is released to Arxiv.

  • [2026.07]  🎉🎉 Our paper “An Empirical Study of Reasoning Degradation in Quantized Multimodal Large Language Models” is accepted by ACM MM 2026.

  • [2026.06]  🎉🎉 Our paper “GreenMoE: Exploiting Dynamic Load Imbalance for Energy-Efficient Long-Context MoE Training” is accepted by ICML 2026 AdaptFM Workshop.

  • [2026.06]  🎉🎉 Our paper “Parameters as Agentic Memory: Internalizing Long-Horizon Memories for Efficient LLM Agents” is accepted by ICML 2026 AIWILD Workshop.

  • [2026.06]  🎉🎉 Our paper “Enhancing Knowledge Injection with Surrounding Backgrounds in Continual Training LLMs” is accepted by ICML 2026 FoGen Workshop.

  • [2026.05]  🎉🎉 Our paper “Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression” is accepted by ICML 2026.

  • [2026.05]  🎉🎉 Our paper “VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing” is accepted by ICML 2026.

  • [2026.05]  🎉🎉 Our paper “Identifying and Mitigating Errors in Gradient Aggregation of Distributed Data Parallel Training” is accepted by ICML 2026.

  • [2026.01]  🎉🎉 Our Paper “Smooth Reading: Bridging the Gap of Recurrent LLM to Self-Attention LLM on Long-Context Understanding” is accepted by ICLR2026.

  • [2025.11]  🎉🎉 Two years after graduation, I was selected as an outstanding master’s student at the NUDT in Hunan Province.

  • [2025.09]  🎉🎉 Our Paper “ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference” is accepted by NeurIPS 2025.

  • [2025.08]  🎉🎉 Our Paper “Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research” is accepted by EMNLP 2025 findings.

  • [2025.08]  🎉🎉 Our Paper “Smooth Reading: Bridging the Gap of Recurrent LLM to Self-Attention LLM on Long-Context Tasks” is released to arxiv.

  • [2025.08]  🎉🎉 Our tech report “Intern-S1: A Scientific Multimodal Foundation Model” is released to arxiv. Great work by Intern-S1 team.

  • [2025.05]  🎉🎉 Our Paper “Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compresssion” is accepted by ICML25. We are especially grateful to the reviewer who awarded us a ‘5 (Strong Accept)’.

  • [2025.04]  🎉🎉 I’ve been invited to be an Area Chair in NeurIPS 2025.

  • [2025.02]  🎉🎉 Congratulations to our team (lead by @Ruibo) to get “SpInfer: Leveraging Low-Level Sparsity for Efficient Large Language Model Inference on GPUs” accepted by EuroSys 2025 as Best Paper !!!

  • [2025.02]  🎉🎉 I am awarded the Excellent Research Prize for the 2024 DSA Excellent Research Award!!!

  • [2025.01]  🎉🎉 Our STBLLM is accepted by ICLR25. STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs, International Conference on Learning Representations, 2025.

  • [2025.01]  🎉🎉 Our Lottery LLM Hypothesis is accepted by ICLR25 Blogpost Oral. The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?, International Conference on Learning Representations Blog Track Oral, 2025.

  • [2025.01]  🎉🎉 Our ParZC is accepted by AAA25 (Oral). ParZC: Parametric Zero-Cost Proxies for Efficient NAS, Association for the Advancement of Artificial Intelligence, 2025.

  • [2024.12]  🎉🎉 I was invited to give a talk to PDL about “Introduction to LLM Compression and Beyond”.

  • [2024.10]  🎉🎉 FuseFL is accepted by NeurIPS 2024 (Spotlight). FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Layer Fusion, Neural Information Processing Systems (NeurIPS) Spotlight, 2024.

  • [2024.10]  🎉🎉 DSA is accepted by NeurIPS 2024, Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models, Neural Information Processing Systems (NeurIPS), 2024.

  • [2024.10]  🎉🎉 Our paper “Should we really edit language models? on the evaluation of edited language models” is accepted by NeurIPS 2024.

  • [2024.10]  🎉🎉 LPZero is accepted by EMNLP 2024. LPZero: Language Model Zero-cost Proxy Search from Zero, Empirical Methods in Natural Language Processing (EMNLP), 2024. (paper, code)

  • [2024.10]  🎉🎉 LongGenBench is accepted by EMNLP 2024. LongGenBench: Long-context Generation Benchmark, Empirical Methods in Natural Language Processing (EMNLP), 2024.

  • [2024.05]  🎉🎉 Pruner-Zero is accepted by ICML 2024. This work evolves symbolic pruning metrics from scratch for large language models. (paper, code)

  • [2024.03]  🎉🎉 VMRNN is available. This work proposes the VMRNN cell, a new recurrent unit that integrates the strengths of Vision Mamba blocks with LSTM. We construct a network centered on VMRNN cells to tackle spatiotemporal prediction tasks effectively. (paper, code)

  • [2023.12]  🎉🎉 KD-Zero is accepted by NeurIPS 2023. This work evolves knowledge distiller for any teacher-student pairs. (paper)

  • [2023.10]  🎉🎉 EMQ is accepted by ICCV 2023. This work evolves training-free proxies for automated mixed precision quantization. (paper, code)

  • [2023.10]  🎉🎉 AutoKD: Automated KD via MCTS is accepted by ICCV 2023. This work proposes automated knowledge distillation via Monte Carlo Tree Search. (paper)

  • [2023.03]  🎉🎉 DisWOT is accepted by CVPR 2023. This work proposes student architecture search for distillation without training. (paper, code)

  • [2023.02]  🎉🎉 Progressive Meta-Pooling Learning is accepted by ICASSP 2023. This work proposes a lightweight image classification model. (paper)

  • [2023.02]  🎉🎉 RD-NAS is accepted by ICASSP 2023. This work enhances one-shot supernet ranking ability via ranking distillation. (paper)

  • [2023.01]  🎉🎉 AutoRF is accepted by MMM 2022. This work proposes auto learning receptive fields with spatial pooling. (paper)

  • [2022.06]  🎉🎉 Prior-Guided One-shot NAS is accepted by CVPR Workshop 2022. This work proposes prior-guided one-shot neural architecture search. (paper)

📖 Educations

  • 2023.09 - now, The Hong Kong University of Science and Technology (Guangzhou), PhD Candidate in Computer Science

    • Supervisor: Prof. Xiaowen Chu
    • Research Interests: Large Language Models, Model Compression

  • 2020.09 - 2023.06, National University of Defence Technology, Master of Engineering

    • Supervisor: Prof. Xin Niu
    • Research Interests: AutoML, Neural Architecture Search
    • Achievement: Outstanding Graduate

  • 2016.09 - 2020.06, Northwest Agriculture & Forestry University, B.S. in Software Engineering

    • GPA: 3.78/4.0 (Ranked 1st out of 93)
    • Advisor: Prof. Hongming Zhang
    • Achievements: National Scholarship, Principal’s Scholarship, Outstanding Graduate
    • Research Interests: Object Detection, Multi-Object Tracking

💻 Internship

  • 06/2026-present: Research Intern, Tencent CSIG WorkBuddy/CodeBuddy - post-training and evaluation for long-horizon coding agents
  • 10/2025–02/2026: Intern, Alibaba – large-scale model training
  • 03/2025–08/2025: Intern, Shanghai AI Lab – AI infrastructure for Xtuner project
  • 05/2022–08/2022: Intern, Shanghai AI Lab – model compression with MMRazor

👔 Professional Activities

  • 2022: ICASSP
  • 2023: NeurIPS, ICASSP, CIM
  • 2024:
    • Conferences: NeurIPS, ICLR, CVPR, ECCV, ICASSP, ACL (ARR)
    • Journals: TPAMI, Neural Networks, Information Fusion, CIM
  • 2025:
    • Conferences: NeurIPS (AC), ICLR, CVPR, ECCV, ICASSP
    • Journals: IJCV, Neural Networks
  • 2026：
    • Conferences: AAAI (PC), WACV, NeurIPS, ICLR
    • Journals: Neural Networks

🎖 Honors and Awards

  • 2024, Best Speaker in DSA Salon 2024.
  • 2023, Outstanding Graduate at School Level, National University of Defense Technology.
  • 2022, 1st Place, BDCI Retail Product Recognition based on MindSpore (CCF Big Data & Computing Intelligence Contest).
  • 2022, 1st Place, DCIC Intelligent Ship Detection Competition (Digital China Innovation Contest).
  • 2022, 2nd Place, DCIC Intelligent Cattle Segmentation Competition (Digital China Innovation Contest).
  • 2022, 1st Place, Baidu AI Competition - Blurred Document Image Recovery.
  • 2022, 3rd Place, Computer Vision and Pattern Recognition (CVPR) Third Workshop on NAS.
  • 2021, Outstanding MindSpore Developer.
  • 2020, Outstanding Dissertation, Northwest A&F University.
  • 2020, Outstanding Graduate, Northwest A&F University.
  • 2017, President’s Scholarship, Northwest A&F University.
  • 2016, National Scholarship, Northwest A&F University.

📝 Publications

Selected papers: EuroSys(Best Paper), AAAIx1(Oral), NeurIPSx1(Spotlight), ICMLx5, EMNLPx1, CVPRx1, ICCVx1, ICASSPx2, ICLRx2(Oralx1).

  • X. Liu, Z. Tang, H. Chen, P. Dong, Z. Li, X. Zhou, B. Li, X. Hu, X. Chu. Semantic Integrity Matters: Benchmarking and Preserving High-Density Reasoning in KV Cache Compression. In ICML 2026.

  • X. Su, P. Dong, Z. Tang, S. Tang, Y. Zhai, K. Lin, L. Chen, Y. Gai, Y. Luo, Q. Wang, X. Chu. VCG-Bench: Towards A Unified Visual-Centric Benchmark for Structured Generation and Editing. In ICML 2026.

  • Z. Tang, J. Huang, Z. Tang, X. Kang, Y. Wang, P. Dong, S. Shi, X. Chu, B. Li. Identifying and Mitigating Errors in Gradient Aggregation of Distributed Data Parallel Training. In ICML 2026.

  • P. Dong, Z. Tang, X. Liu, L. Li, X. Chu, B. Li. Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression. In ICML2025.

  • R. Fan, X. Yu, P. Dong, Z. Li, G. Gong, Q. Wang, W. Wang, X. Chu. SpInfer: Leveraging Low-Level Sparsity for Efficient Large Language Model Inference on GPUs. In EuroSys2025, Best Paper.

  • P. Dong, L. Li, Z. Tang, X. Liu, Z. Wei, Q. Wang, X. Chu. ParZC: Parametric Zero-Cost Proxies for Efficient NAS. In AAAI2025, Oral.

  • P. Dong, L. Li, Y. Zhong, D. Du, R. Fan, Y. Chen, Z. Tang, Q. Wang, W. Xue, Y. Guo, X. Chu. STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs. In ICLR2025.

  • L. Li, P. Dong, Z. Tang, X. Liu, X. Pan, X. Chu. Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models. In NeurIPS 2024.

  • Q. Li, X. Liu, Z. Tang, P. Dong, Z. Li, X. Pan, X. Chu, Should We Really Edit Language Models? On the Evaluation of Edited Language Models. In NeurIPS 2024.

  • P. Dong, L. Li, Z. Tang, X. Liu, X. Pan, Q. Wang, X. Chu. Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models. In ICML 2024.

  • P. Dong, L. Li, X. Liu, Z. Tang, X. Liu, Q. Wang, X. Chu. LPZero: Language Model Zero-cost Proxy Search from Zero, Empirical Methods in Natural Language Processing (EMNLP), 2024.

  • X. Liu, P. Dong, X. Hu, X. Chu. LongGenBench: Long-context Generation Benchmark. In EMNLP 2024.

  • Z. Tang, Y. Zhang, P. Dong, Y. Cheung, A. C. Zhou, B. Han, X. Chu. FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Layer Fusion. In NeurIPS Spotlight 2024.

  • P. Dong, L. Li, Z. Wei. DisWOT: Student Architecture Search for Distillation without Training. In CVPR 2023.

  • P. Dong, L. Li, Z. Wei, X. Niu$^*$, Z. Tian, H. Pan. EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization. In ICCV 2023.

  • L. Li, P. Dong, A. Li, Z. Wei, Y. Yang. Kd-zero: Evolving knowledge distiller for any teacher-student pairs. In NeurIPS 2023.

  • P. Dong, X. Niu, Z. Tian, et al. Progressive Meta-Pooling Learning for Lightweight Image Classification Model. In ICASSP 2023.

  • P. Dong, X. Niu, L. Li, et al. RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation. In ICASSP 2023.

  • P. Dong, X. Niu, H. Pan, et al. AutoRF: Auto Learning Receptive Fields with Spatial Pooling. In MMM 2023.

  • P. Dong, X. Niu, L. Li, et al. Prior-Guided One-shot Neural Architecture Search. In CVPR Workshop 2022.

  • L. Li, P. Dong, Z. Wei, Y. Ya. Automated Knowledge Distillation via Monte Carlo Tree Search. In ICCV 2023.