We propose TraceRL, a trajectory-aware reinforcement learning framework fordiffusion language models (DLMs) that incorporates preferred inferencetrajectory into post-training, and is applicable across differentarchitectures. Equipped with a diffusion-based value model that enhancestraining stability, we demonstrate improved reasoning performance on complexmath and coding tasks. Besides, it can also be applied to adapt block-specificmodels to larger blocks, which improves sampling flexibility. EmployingTraceRL, we derive a series of state-of-the-art diffusion language models,namely TraDo. Although smaller than 7B-scale AR models, TraDo-4B-Instruct stillconsistently outperforms them across complex math reasoning tasks.TraDo-8B-Instruct achieves relative accuracy improvements of 6.1% overQwen2.5-7B-Instruct and 51.3% over Llama3.1-8B-Instruct on mathematicalreasoning benchmarks. Through curriculum learning, we also derive the firstlong-CoT DLM, outperforming Qwen2.5-7B-Instruct on MATH500 with an 18.1%relative accuracy gain. To facilitate reproducible research and practicalapplications, we release a comprehensive open-source framework for building,training, and deploying diffusion LLMs across diverse architectures. Theframework integrates accelerated KV-cache techniques and inference engines forboth inference and reinforcement learning, and includes implementations ofvarious supervised fine-tuning and RL methods for mathematics, coding, andgeneral tasks. Code and Models: https://github.com/Gen-Verse/dLLM-RL