arXiv:2605.26184v1 Announce Type: new 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
The continuous improvement of AI models necessitates more efficient and adaptive training methods, with current fixed mixing schedules being a recognized limitation. This development addresses the inherent noise and dynamic nature of combined SFT-RL signals.
Adaptive mixing techniques like GAC can lead to more robust and performant AI models by optimizing the learning process, directly impacting the quality and capability of future AI systems. This specifically advances the state-of-the-art in hybrid post-training for large language models and other AI.
The prior fixed mixing of supervised fine-tuning and reinforcement learning is replaced by a dynamic, noise-aware approach, allowing AI training to adapt to changing signal qualities and accelerate model development and refinement. This improves the efficiency of advanced AI training.
- · AI model developers
- · Companies deploying advanced AI
- · AI research institutions
- · Developers relying on static training methodologies
- · AI models with suboptimal training
Improved efficiency and performance in advanced AI model training and deployment.
Faster development cycles for cutting-edge AI applications, particularly in complex reasoning tasks.
Accelerated progress towards more capable and autonomous AI agents across various domains.
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Read at arXiv cs.LG