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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unpublished preprint describing a proposed algorithmic modification (TV-OPD) to on-policy distillation in large language models. The authors report that their method reduces training variance and improves late-stage performance in computational experiments, but provide no peer review, no quantitative results, no comparator effect sizes, and no information on experimental replication or scope.
Preprint. Intervention: Total Variation (TV) regularization applied to token-level advantages in on-policy distillation (TV-OPD).
Retaining only the sign of token-level advantages achieves performance comparable to standard OPD Smoother and bounded advantages stabilize training without sacrificing performance TV-OPD exhibits stable training dynamics and steady late-stage performance across various settings
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Unrefereed computational methods paper on machine learning optimization; no clinical trial data, human subjects, or peer-reviewed publication status reported.
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On-Policy Distillation (OPD) facilitates the transfer of knowledge from domain expert to student in the post-training phase of Large Language Models (LLMs). However, the supervision signals in mainstream OPD methods suffer from high variance and noise which is generally instable during training. In this work, we systematically investigated what really matters to the performance and the fundamental mechanisms behind the instability during training. We found that retaining only the sign of token-level advantages is sufficient to achieve the performance comparable to standard OPD. Meanwhile, smoother and bounded advantages can stabilize the training process without sacrificing its performance. These motivated us to shape the advantages using the Total Variation (TV) and propose a robust TV regulated On-Policy Distillation (TV-OPD) method. Benefiting from the bounded and diminished advantages, TV-OPD exhibits stable training dynamics and steady late-stage performance. We conducted comprehensive experiments and found that, across various settings, TV-OPD consistently achieved better performance and lower variance in the late-stage of training.
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