arXiv:2605.25966v1 Announce Type: cross Abstract: We test whether the optimal learning-rate schedule depends on bit-width during from-initialisation quantisation-aware training (QAT) for sub-100M decoder language models. A 720-run factorial grid (Phase 2) over bit-width x warmdown fraction x LR magnitude x model size x seed (FP16/INT8/INT6, 15M-100M, 5 seeds) finds the optimal warmdown is 33% at every (bit-width, size) cell. The primary hypothesis -- that INT6 QAT requires a different schedule than higher-precision training -- is falsified at FP16/INT8/INT6. A 625-run follow-up (Phase 5) probe
The continuous push for smaller, more efficient AI models is driving research into quantization techniques as a core method to reduce computational and memory overhead.
This research provides crucial insights into optimizing training schedules for highly quantized language models, potentially making powerful AI more accessible and energy-efficient.
The findings suggest that the optimal learning-rate schedule for sub-100M language models remains consistent across different precision levels (FP16/INT8/INT6), simplifying development but also indicating a potential ceiling for further schedule optimization in low-bit QAT.
- · AI developers
- · Edge AI hardware manufacturers
- · Energy-conscious AI deployments
- · Developers solely focused on high-precision models
- · Hardware optimized only for FP16/FP32
More efficient and compact AI models become practical for deployment on resource-constrained devices.
Reduced computational costs for training and inference could accelerate AI development and innovation in new applications.
The widespread adoption of highly efficient, smaller models might decentralize AI power, potentially reducing reliance on massive, centralized compute infrastructure.
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