On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning

Yu Li, Yufa Zhou, Tian Lan

On-policy reinforcement learning for large language models assumes that training responses are sampled from the current policy. In practice, an inference engine samples the responses and a separate trainer computes the gradients, and the \textbf{training-inference mismatch} between the two makes the training data off-policy. The mismatch has two sources, the numerics of the engine and the stale weights of asynchronous sampling, and a correction that stabilizes training under one source can fail under the other. \textit{We ask which property of a mismatch source decides the correction it needs.} Evaluating the trainer at the sampler's weights splits each sampled-token error exactly into a \textit{numerical discrepancy} of the engine and a \textit{policy lag} of the trainer, and we follow both through training. The numerical discrepancy stays approximately constant, so it biases the updates by a nearly fixed amount, and centering the score at each prefix removes its leading bias. The policy lag is produced by the trainer's own updates: the bias it leaves enters the next update, which enlarges the next lag and closes a feedback loop. Importance weighting cuts this loop only while…