Reinforcement learning has become the standard for large-model post-training (GRPO, DPO, PPO, etc.), but "training runs and then crashes" is still an unsolved engineering problem. The industry has long attributed the root cause to off-policyness and KL drift, then added entropy regularization, clip, and reference policy in the loss function — but it still crashes. The paper "The Mirage of Optimizing Training Policies" (Liang et al., including Tencent researcher Bo Zheng), posted to arXiv on June 28, 2026, gives a different interpretation: all existing optimization work has been contaminated by a neglected "objective misalignment". LLM RL uses the inference engine during the rollout phase, and the training engine during the policy update phase; even if the two engines' weights are synchronized, they give inconsistent token probabilities for the same trajectory, this is called "Training-Inference Mismatch (TIM)". Previous work treats TIM as off-policyness noise to suppress, this paper instead points out that TIM is an independent structural bias: an update that "looks effective" in the training engine may not actually be better when deployed to the inference engine. Based on this, the authors propose two components: - Monotonic Inference Policy Improvement (MIPI) — explicitly writing "let the deployed inference policy monotonically improve" into the optimization objective; - Monotonic Inference Policy Update (MIPU) — a two-step framework: first use a sampler to construct candidate updates, then use the inference-side gap proxy to selectively accept sync candidates. The paper is measured on two model scales under high-mismatch conditions, MIPU achieves observable improvement in both average inference performance and training stability, and reduces the frequency of training crashes. My view: the significance of this isn't those few metrics, but pulling RL post-training engineering to the "first-principles" level. All teams doing GRPO / RLVR / Online DPO are advised to re-examine "training-inference consistency" — it may affect the final delivered quality more than the trained policy itself.