arXiv:2606.09091v1 Announce Type: new 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 Distill
The paper addresses a critical technical challenge in on-policy distillation, a post-training paradigm gaining traction for improving large language models, especially in reasoning tasks.
Improving the stability and effectiveness of on-policy distillation can significantly enhance the reasoning capabilities and reliability of multimodal large language models (MLLMs), broadening their applicability.
The proposed 'Globally Normalized Distill' method offers a solution to gradient instability in token-level distillation, potentially leading to more robust and higher-performing MLLMs.
- · AI researchers
- · MLOps platforms
- · Companies developing MLLMs
- · Sectors requiring sophisticated AI reasoning
- · Developers relying on unstable distillation methods
- · Systems with brittle MLLM reasoning
More reliable and advanced AI models emerge, particularly in multimodal reasoning tasks.
This improved AI capability accelerates the development and deployment of autonomous AI agents.
Enhanced AI reasoning contributes to further automation of complex tasks, impacting white-collar workflows and the SaaS industry.
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Read at arXiv cs.LG