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Scaling QeRL to >70B-parameter LLMs

Determine whether the QeRL framework—comprising NVFP4 weight quantization integrated with Low-Rank Adaptation and Adaptive Quantization Noise—maintains the same level of reinforcement learning performance when applied to large language models with parameter counts exceeding 70 billion, as observed in experiments on 3B–32B models.

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Background

QeRL combines NVFP4 quantization with LoRA to reduce memory and accelerate rollouts during reinforcement learning for LLMs, and introduces Adaptive Quantization Noise to enhance exploration. The paper demonstrates strong performance and speedups across 3B–32B models on math reasoning benchmarks compared to 16-bit LoRA and QLoRA, often approaching full-parameter RL fine-tuning.

However, due to the resource intensiveness of RL, the authors did not evaluate models larger than 32B parameters, noting that it is not yet established whether QeRL can maintain its performance for models exceeding 70B parameters. This leaves open the question of QeRL’s scalability to very large LLMs.

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, Limitation Analysis