Tianyu
Tianyu is an individual researcher and solo developer/creator, Tianyu Chen (GitHub handle TianyuCodings), whose work builds reinforcement-learning and distillation methods to make LLMs and multimodal agents reason, search, and evaluate reliably. The portfolio combines academic research in statistics with open-source research software, benchmarks, models, and datasets.
At a Glance
- Academic machine-learning and statistics researchers
- Open-source AI/ML developers
- Teams developing LLM post-training, agents, retrieval, and evaluation systems
- Researchers and developers working on image generation/editing and multimodal models
- +1 more
AI Tools by Tianyu
(1)
NanoJev
Open Source Parallel Decision Model
Discussions
No discussions yet
Be the first to start a discussion about Tianyu
Latest News
Published the preprint Test-Time Optimization of Query Embeddings with Ranking Aware Reward Maximization, from work at Google DeepMind.
Received the 2026 Intern and Student Researcher Award at Google DeepMind.
Announced MT-EditFlow, reinforcement learning for multi-turn image editing with flow matching.
Announced acceptance of REAL and Conformal C2ST to ICML 2026.
Products & Services
Open-source nano replica of Jev: a Qwen3-0.6B parallel decision model with zero output-token decoding, dynamic candidate distributions, Boolean and ordered scores, decision heads, a training pipeline, public Hugging Face model/data releases, and demos across ViZDoom, Maze, and Snake.
Open-source toolkit that lets an LLM build task-specific Jev decision harnesses, with optional refinement using rewards and execution traces; it includes a Pokémon demonstration and Claude Code plugin packaging.
Object-centric VLM-agent framework and benchmark for scalable, fine-grained evaluation of multi-turn image editing. It generates multi-turn instructions, runs editing models, and evaluates instruction-following, consistency, and perceptual quality; EdiVal-Bench covers nine instruction types and 16 editing models.
Research method and official PyTorch implementation for offline reinforcement learning using diffusion policies to create a trust region.
Market Position
This is a research portfolio rather than a commercial company. NanoJev positions itself as a small open-source replica/alternative to Jev and is evaluated against Jev and untuned Qwen3-0.6B on Maze, Snake, and ViZDoom tasks. EdiVal provides a benchmark and evaluator for comparing leading image-editing systems including Seedream 4.0, Nano Banana, GPT-Image-1, FLUX, Gemini, Qwen-Image-Edit, and others. The broader work differentiates through statistically grounded reinforcement learning, distillation, agentic retrieval, and automated evaluation.
Leadership
Founders
Tianyu Chen
Statistics PhD candidate at the University of Texas at Austin (2023–2028 expected), previously an M.S. Statistics student at the University of Chicago and a B.S. Statistics graduate of Fudan University. Chen has been a Student Researcher at Google DeepMind (Gemini) since March 2026 and was a Research Scientist Intern at Microsoft Research (MAI Superintelligence) from February 2025 to March 2026.
Business Model
Revenue Model
The available sources describe academic research and open-source software/models/datasets rather than a commercial revenue model. NanoJev is MIT-licensed, and the repositories provide public code and Hugging Face releases.
Target Markets
- Academic machine-learning and statistics researchers
- Open-source AI/ML developers
- Teams developing LLM post-training, agents, retrieval, and evaluation systems
- Researchers and developers working on image generation/editing and multimodal models
- Offline reinforcement-learning and generative-modeling practitioners
- LLM post-training and alignment
- LLM-as-a-Judge reward modeling and evaluation
- Agentic multi-hop text and visual retrieval
- Automated evaluation of multi-turn image-editing systems
- Reinforcement-learning agents for games and other discrete decision tasks
- Offline reinforcement learning with diffusion policies