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Life sciences · Preprint
arXiv · August 18, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed preprint proposing GUPO, a Bayesian gradient-aggregation method for LLM post-training that models group-gradient conflicts using uncertainty estimation. The work has not undergone peer review and provides no clinical outcome data, making it unsuitable for evidence-based clinical decision-making.
Preprint. Intervention: Gradient Uncertainty-Aware Policy Optimization (GUPO), a Bayesian gradient-aggregation method that estimates group-gradient probability distributions and uses Dirichlet-based uncertainty to calibrate contribution of each group gradient dur…. Compared with: Group Relative Policy Optimization (GRPO).
Empirical analysis indicates group-gradient conflicts are associated with less effective policy updates in GRPO GUPO models each group gradient as a random variable under Bayesian formulation with Dirichlet-based uncertainty estimation Extensive experiments on multiple benchmarks demonstrate effectiveness of GUPO
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This is an unrefereed arXiv preprint describing a machine learning algorithmic innovation without peer review or clinical/biological validation data.
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Group Relative Policy Optimization (GRPO) has become a widely used approach for post-training Large Language Models (LLMs) for reasoning. In GRPO, the group gradients induced by different queries within the same mini-batch are directly averaged to form the policy update. However, these group gradients can point in conflicting directions. Our empirical analysis suggests that group-gradient conflicts tend to be associated with less effective policy updates, motivating the need for a reliable aggregated update direction under such conflicts. Standard GRPO aggregation treats the realized group gradients as deterministic contributions and does not account for differences in their reliability during aggregation. To address this issue, we propose Gradient Uncertainty-Aware Policy Optimization (GUPO), which models each group gradient as a random variable under a Bayesian formulation and estimates its probability distribution. GUPO then derives gradient uncertainty using a Dirichlet-based formulation and uses it to calibrate the contribution of each group gradient during aggregation. Extensive experiments on multiple benchmarks demonstrate the effectiveness of GUPO.
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