GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training
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Computer Science > Machine Learning
Title:GAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training
Abstract:Hybrid post-training usually combines supervised fine-tuning and reinforcement learning, but fixed mixing schedules cannot adapt when the relative noise of the two signals changes over time. We propose GAC, a noise-aware controller that derives an adaptive mixing weight from online estimates of gradient variance and disagreement between the two training signals. The method adds smoothing, prior guidance, and bounded updates while reusing existing training tensors. Experiments on math, code, science, and logic benchmarks show that GAC consistently improves hybrid post-training over strong fixed and rule-based baselines, with larger gains at larger model scales and less than 1% training overhead.
| Comments: | 15 pages, 3 figures, 22 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.26184 [cs.LG] |
| (or arXiv:2605.26184v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.26184
arXiv-issued DOI via DataCite
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