Stabilizing On-Policy Distillation for MLLM Reasoning with Global Normalization

Abstract:On-policy distillation (OPD) has recently emerged as an important post-training paradigm. By using a stronger teacher model to provide dense, fine-grained supervision for sampled trajectories, OPD offers a clear advantage over reinforcement learning with verifiable rewards (RLVR), which typically depends on sparse binary or outcome-based environmental feedback. However, naive token-level distillation can suffer from gradient instability, due to magnitude misalignment in outlier states. To address this issue, we propose Globally Normalized Distillation Policy Optimization (GNDPO), a practical method that stabilizes optimization by transforming raw KL scores into batch-level relative advantages. This normalization effectively mitigates gradient explosions while retaining the benefits of token-level guidance. Experimental results show that GNDPO substantially improves training robustness and downstream performance across multimodal reasoning tasks. The code is released at this https URL.

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2606.09091 [cs.LG]

(or arXiv:2606.09091v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.09091 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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