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arXiv · 2606.09091

Stabilizing On-Policy Distillation for MLLM Reasoning with Global Normalization

Dongze Hao·Zhiwei Jin·Chen Chen·Haonan Lu
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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 https://github.com/OPPO-Mente-Lab/GNDPO.

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Dongze Hao, Zhiwei Jin, Chen Chen, Haonan Lu. 2026-06-08. Stabilizing On-Policy Distillation for MLLM Reasoning with Global Normalization. https://arxiv.org/abs/2606.09091

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