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Scalability of QeRL beyond 70B-parameter models

Determine whether QeRL—an NVFP4-quantized, LoRA-based reinforcement learning framework with adaptive quantization noise—can maintain the same level of performance when applied to large language models exceeding 70 billion parameters as it demonstrates on 3B–32B models for reasoning-focused reinforcement learning tasks.

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Background

QeRL combines NVFP4 weight quantization with LoRA fine-tuning and an adaptive quantization noise mechanism to accelerate reinforcement learning rollouts while enhancing exploration. Across 3B–32B models, QeRL matches or surpasses 16-bit LoRA and approaches full-parameter fine-tuning performance on reasoning benchmarks.

Due to resource constraints inherent to RL training, the experiments do not cover models larger than 32B. The authors explicitly state that it remains unresolved whether QeRL preserves its performance at scales exceeding 70B parameters and leave this investigation for future work, highlighting a key scalability question.

References

However, since RL for LLMs inherently demands significantly greater computational resources than SFT, our experiments, conducted on model sizes ranging from 3B to 32B, do not yet establish whether QeRL can maintain the same level of performance for models exceeding 70B parameters, leaving that investigation for future work.

— QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs (2510.11696 - Huang et al., 13 Oct 2025) in Appendix, Section “Limitation Analysis”