Diffusion LLM (dLLM) RL training has long been paying for the mismatch between "random masking target" and "inference trajectory" — the current SoTA TraceRL cuts each rollout into K/s trajectory-aligned samples, with training cost growing linearly with block size K. SLIM-RL, proposed in arXiv:2607.00208, takes a different path: rather than reconstructing trajectories at all, it uses a τ-budget decoder to set a commit-risk upper bound for each step's rollout, suppressing mismatched loss at the data source. The companion trace-free random-masking objective combines sequence-level importance sampling with deterministic quadrature on masking levels, plus a "per-block uniform hold, monotonically decreasing" mask schedule, effectively reducing variance. Results on SDAR-4B with 0.46× training samples reproduce TraceRL's best MATH500; under matched dynamic sampling, MATH500 improves 6.32%, GSM8K improves 11.05%. More critically, the scale inversion: the 4B model trained with SLIM-RL directly surpasses the larger LLaDA-8B (MATH500 +10.76%) and Dream-7B, but still slightly below the autoregressive baseline Qwen2.5-7B — indicating the RL dividend of dLLM is far from tapped. At the same time, the τ-budget decoder transfers across LLaDA, Dream, and SDAR without retraining, meaning the same RL recipe can be reused across multiple open-source dLLMs. Code is open-sourced (github.com/laolaorkkkkk/SLIM-RL). This gives the industry a clear signal: the future of diffusion LLM RL may not be cutting rollouts finer, but coupling "risk budget" with "random masking" — a cheaper, more transferable training paradigm is replacing trajectory engineering.