Tinker: Thinking Machines Lab Thoughts (pranavc28.github.io)

A developer used Tinker (Thinking Machines Lab’s fine‑tuning tool) to adapt Qwen / Qwen3‑30B‑A3B on a public React/TypeScript dataset (React‑code‑instructions) and produced generally usable React components at far lower cost than top‑tier labs. The writeup argues Generative UI is a distinct conditional generation task—requiring syntactic correctness, visual grounding, stateful interactivity and adherence to style/accessibility constraints—so off‑the‑shelf supervised fine‑tuning often sucks in mixed‑quality examples. By fine‑tuning with Tinker, the author demonstrates practical, reproducible gains in UI code quality and suggests teams can cheaply customize LLMs to their own design systems and codebases. Technically, the project replaces supervised fine‑tuning with Group Relative Policy Optimization (GRPO): for each prompt it samples k completions, scores them with a multi‑term reward (completeness, syntactic validity, interactivity/state usage, balanced quotes, length penalties), computes advantages relative to the group mean, and updates the policy to favor above‑average trajectories. GRPO stabilizes updates via group normalization rather than value nets or PPO clipping, and the implementation uses asynchronous sampling to scale throughput. The result highlights key implications for the AI/ML community: specialized RL objectives and reward design are crucial for generative UI, exploration can discover better patterns than noisy datasets, and lightweight open‑source fine‑tuning pipelines can make production‑grade UI generation accessible.