During two months contributing to google/tunix, Daihanjun delivered nine features and a stability fix focused on backend robustness and configuration flexibility. He improved GrpoPipeline by introducing error handling for missing lora_config, reducing runtime failures in production. In December, Daihanjun enhanced configuration clarity, streamlined tokenization, and enabled external data and reward module imports, supporting more reproducible experiments. His work included refining model architecture for Qwen3-4B, adding learning rate telemetry, and improving JAX array handling for sampling utilities. Using Python, YAML, and NumPy, Daihanjun’s contributions demonstrated depth in backend development, automated testing, and configuration management, resulting in more reliable and extensible workflows.