Large language models (LLMs) for code generation have achieved remarkable progress in synthesizing functional code from natural language instructions. However, a critical challenge persists in generating visually accurate and structurally sound front-end code that faithfully renders user-intended layouts and interfaces. Most existing works focus primarily on functional correctness, overlooking the visual fidelity and rendering quality essential for front-end development. To address this gap, we present a comprehensive data construction and training pipeline to enhance front-end code generation capabilities in code LLMs. We use a three-stage training approach: continual pre-training on synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning with checklist-based rewards to improve model performance. Our comprehensive evaluation on front-end code generation benchmarks reveals that even strong base models struggle with visual faithfulness and layout complexity. Our fully-trained model demonstrated substantial improvements over baseline approaches across all domains, achieving competitive performance with frontier models while maintaining generation efficiency, underscoring the critical importance of stage-aligned data curation and vision-grounded optimization in developing reliable front-end code generation systems. Our code and data are open-sourced at https://github.com/leanfeng1/FrontCoder.

Industrial code tasks, including chip design, GPU optimization, and firmware, often fail for reasons that are invisible in ordinary source code: timing constraints, backend logs, layout data, simulator behavior, and hardware-specific performance trade-offs. Current code models see many programs but few expert correction traces from these settings. We present InCoder-32B-Thinking, a 32B thinking code model trained with Error-driven Chain-of-Thought (ECoT) synthesis and an Industrial Code World Model (ICWM). ECoT converts several rounds of toolchain signals into reasoning traces by preserving the failed code, the observed failure, and the later fix. ICWM learns from execution traces in domains such as Verilog simulation and GPU profiling, then predicts feedback for the data engine, which can build more repair traces without routing every candidate through a real toolchain. The data keeps the uneven reasoning depth of industrial work: short traces for simple cases, long traces for hardware-level debugging. Across 23 tests (14 general and 9 industrial), InCoder-32B-Thinking reaches 81.3% on LiveCodeBench v5, 84.0% CAD-Coder compile success, and 38.0% on KernelBench L2, while remaining competitive on general coding tasks. Model weights and code are available at Hugging Face: https://huggingface.co/Multilingual-Multimodal-NLP/IndustrialCoder and GitHub: https://github.com/CSJianYang/Industrial-Coder.