Shaoyang Guo 郭绍阳

Curriculum Vitae

Shaoyang Guo 郭绍阳

Physics undergraduate at Peking University (Class of 2027). Research intern at ByteDance Seed. Working on VLM post-training, STEM reasoning evaluation, and data-centric optimization.

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Education

B.S. in Physics, Peking University (expected 2027). Admitted via Excellence Program (CPhO Gold Medal). Top 10% in first-year cohort. 141/149 credits by sophomore year including 3 graduate courses.

Peking University, School of Physics

B.S. in Physics (expected 2027). Admitted via Excellence Program (CPhO Gold Medal). Top 10% in first-year physics cohort. Completed 141/149 credits by sophomore year including 3 graduate courses.

National Scholarship (2024)

Ministry of Education, top 1% at Peking University.

Chinese Physics Olympiad Gold Medal

National rank #57 (2022). Admitted to PKU Physics via Excellence Program.

NOIP First Prize (2020)

National Olympiad in Informatics in Provinces.

Research Experience

Jul 2025 – Present

Research Intern, Seed VLM Post-Training Team

ByteDance

Working on multi-stage post-training for vision-language models, targeting STEM reasoning capabilities through RL, SFT, and mid-training.

  • Responsible for multi-stage delivery across RL, SFT, and mid-training; participated in early mid-training exploration.
  • Researched BoN sampling and sample-repeat strategies; disproved equilibrium-based sampling assumptions; established RL–SFT equivalence under repeat conditions.
  • Developed large-scale data cleaning pipeline with probabilistic quality models; built textbook exercise extraction system (100M+ QA pairs).
  • Exploring TransferRL (combining SFT with RL) and CoT compression methods for efficient reasoning synthesis.
Feb 2025 – Sep 2025

Co-initiator & Co-first Author, PHYBench

Peking University (Eureka Lab)

Co-initiated and co-led PHYBench, a physics reasoning benchmark for LLMs. Paper submitted to NeurIPS 2025.

  • Identified gaps in existing LLM physics evaluation; led project from concept validation to full data pipeline.
  • Organized 178 PKU students to build 500 high-quality original physics problems in 2 weeks.
  • Designed evaluation criteria, quality control processes, and failure mode analysis frameworks.
  • Completed main experiments and analysis of frontier LLM performance across physics subdomains.
Mar 2025 – Aug 2025

Research Assistant, VLA Survey

PsiRobot Lab, Peking University

Co-authored a survey on Vision-Language-Action models. Advisor: Prof. Yaodong Yang.

  • Responsible for the Raw Action chapter; reviewed 30+ key papers on end-to-end VLA architectures.
  • Organized taxonomies for VLA model design from an action tokenization perspective.

Skills

Programming

Python, PyTorch, C/C++, Shell, MATLAB

ML/AI

RL, SFT, mid-training, evaluation methodology, data quality analysis

Physics

Quantum Mechanics, Quantum Field Theory, Statistical Physics, Computational Physics

Languages

Chinese (native), English (CET-6: 657, GRE: 150+170)

Research Interests & Publications

Focus Areas

  • Physics of AI
  • Embodied Intelligence
  • Vision-Language Models