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This research identifies Training-Inference Mismatch (TIM) as a critical, non-benign factor causing LLM reinforcement learning instability, rather than mere numerical noise. By isolating TIM in a zero-mismatch diagnostic setting, the authors demonstrate that minor token-level discrepancies between rollout generation and policy optimization can independently trigger training collapse. The study provides actionable remedies to mitigate these systems-level perturbations, offering essential guidance for building robust next-generation AI agents.
Diagnosing Training Inference Mismatch in LLM Reinforcement Learning via a Zero-Mismatch Reference
arXiv:2605.14220v2 Announce Type: replace-cross Abstract: Modern LLM RL systems separate rollout generation from policy optimization. These two stages are expected to produce token probabilities that match exactly. However, implementation differences can make them assign different values to the same sequence under the same model weights, inducing Training-Inference Mismatch (TIM). TIM is difficult to inspect because it is entangled with off-policy drift and common stabilization mechanisms. In this work, we isolate TIM in a zero-mismatch diagnostic setting (VeXact), and show that small token-level numerical disagreements can independently cause training collapse. We further show that TIM changes the effective optimization problem, and identify a set of remedies that could mitigate TIM. Our results suggest that TIM is not benign numerical noise, but a systems-level perturbation that should be treated as a first-order factor in analyzing LLM RL stability.
Abstract:Modern LLM RL systems separate rollout generation from policy optimization. These two stages are expected to produce token probabilities that match exactly. However, implementation differences can make them assign different values to the same sequence under the same model weights, inducing Training-Inference Mismatch (TIM). TIM is difficult to inspect because it is entangled with off-policy drift and common stabilization mechanisms. In this work, we isolate TIM in a zero-mismatch diagnostic setting (VeXact), and show that small token-level numerical disagreements can independently cause training collapse. We further show that TIM changes the effective optimization problem, and identify a set of remedies that could mitigate TIM. Our results suggest that TIM is not benign numerical noise, but a systems-level perturbation that should be treated as a first-order factor in analyzing LLM RL stability.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2605.14220 [cs.LG]
(or arXiv:2605.14220v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.14220 Focus to learn more arXiv-issued DOI via DataCite
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