InCoder-32B-Thinking: Industrial Code World Model for Thinking

TL;DR

InCoder-32B-Thinking introduces an industrial code world model trained on reasoning traces from error-driven synthesis, enabling better understanding and verification of hardware-aware code generation.

Contribution

It presents a novel training framework combining error-driven reasoning with domain-specific execution traces for industrial code tasks.

Findings

Achieves 81.3% accuracy on LiveCodeBench v5

Scores 84.0% on CAD-Coder industrial benchmarks

Outperforms existing open-source models across domains

Abstract

Industrial software development across chip design, GPU optimization, and embedded systems lacks expert reasoning traces showing how engineers reason about hardware constraints and timing semantics. In this work, we propose InCoder-32B-Thinking, trained on the data from the Error-driven Chain-of-Thought (ECoT) synthesis framework with an industrial code world model (ICWM) to generate reasoning traces. Specifically, ECoT generates reasoning chains by synthesizing the thinking content from multi-turn dialogue with environmental error feedback, explicitly modeling the error-correction process. ICWM is trained on domain-specific execution traces from Verilog simulation, GPU profiling, etc., learns the causal dynamics of how code affects hardware behavior, and enables self-verification by predicting execution outcomes before actual compilation. All synthesized reasoning traces are validated through domain toolchains, creating training data matching the natural reasoning depth distribution of industrial tasks. Evaluation on 14 general (81.3% on LiveCodeBench v5) and 9 industrial benchmarks (84.0% in CAD-Coder and 38.0% on KernelBench) shows InCoder-32B-Thinking achieves top-tier open-source results across all domains.GPU Optimization