Generalization of Manual Training–Inference Alignment Across Frameworks and Models

Determine whether manual alignment of training and inference implementations to mitigate the training–inference policy mismatch in large language model reinforcement learning can be generalized across different reinforcement learning frameworks and across different language model families.

Background

Modern reinforcement learning frameworks for LLMs use distinct engines for training and inference, which introduces a numerical mismatch between the training policy and the inference policy. Prior work has proposed algorithmic corrections (e.g., token-level truncated importance sampling and sequence-level masked importance sampling) but these add computational overhead and do not eliminate the deployment gap.

An alternative engineering approach manually aligns training and inference implementations to reduce numerical discrepancies. While promising in specific cases, this strategy requires deep domain knowledge and substantial engineering effort, and the authors explicitly note uncertainty about whether such bespoke fixes can generalize across frameworks and model families.