Tianle Li
Member of Technical Staff @ Thinking Machines Lab
About
I work on the toughest problems standing between us and machines with superintelligence. I like training models and make them cracked. I have worked on / contributed to almost all of the model training stacks: science for new RL paradigms, RL recipes & mixtures & infra, large production training runs for Grok 4.1 and Grok 4 Fast, post-training RL, co-led large production distillation, improve reasoning efficiency via RL, model tool use, production evals, improve model presentation / styles, training reward models, human data, AI tutors babysitting, synthetic data, wrote system prompt for Grok 4, and more.
United States
Berkeley
Computer Software
Deep Learning, Large Language Models (LLM)
Experience
Member of Technical Staff
Thinking Machines Lab
San Francisco Bay Area
Member of Technical Staff
xAI
Palo Alto, CA
RL Science & Scaling Team. - Grok 4.2 (Co-creator; model merging lead) - Grok 4.1 (Co-creator; post-training RL lead) - Grok 4.1 Fast (Co-creator; post-training RL lead) - Grok 4 Fast (Co-creator; post-training RL lead; distillation co-lead) - Grok 4 (Core contributor)
Student Researcher
LLM Reasoning
Machine Learning Engineer
Nexusflow
LLM Post-training; Acquired by Nividia
Undergraduate Researcher
Berkeley Sky Computing Lab
Berkeley, California, United States
Building Chatbot Arena, the largest community platform for evaluating LLMs. Advisors: Ion Stoica.
EECS127 Teaching Assistant
UC Berkeley Electrical Engineering & Computer Sciences (EECS)
EECS127/227A Convex Optimization for Machine Learning
Software Development Intern
AMD
AMD Vitis AI Vitis Model Composer: AI powered DSP library tool optimized with AMD Deep Learning Processor Unit.
Software Engineer Intern
Ingram Micro
Irvine, California, United States
Enterprise Architecture and API Governance/Development
Content Creator
YouTube
Make Java, Python, and Game Development tutorials
Education
University of California, Berkeley
Electrical Engineering and Computer Science
Completed Courseworks: CS 189 - Machine Learning CS 182 - Deep Neural Networks CS 285 - Deep Reinforcement Learning (Graduate Course) EECS 106A - Intro to Robotics EECS 127 - Optimization Models EECS 126 - Probability Theory and Random Process EE 120 - Signals and System CS 61A - Intro to CS CS 61B - Data Structure CS 61C - Computer Architecture CS 70 - Discrete Math and Probability EECS 16 A&B Devices and System Design
Tianle Li's Contact Information
Phone
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