The evolution of large language models (LLMs) has led to increasingly complex architectures, such as Mixture-of-Experts (MoE). These architectures allow scaling the number of parameters without proportionally increasing computational cost, but introduce new challenges in reinforcement learning (RL). Specifically, rollout generation — the trajectories explored by the agent during training — becomes a bottleneck due to the large number of inferences required. To accelerate this process, the industry has turned to low-precision quantization, with FP8 being a recent standard. However, formats like NVFP4 promise even higher performance by operating at FP4 precision with native W4A4 GEMM operations.
NVFP4 combines fine-grained scaling for accuracy preservation with higher throughput than FP8, but its application in RL for MoE is not trivial. Experiments reveal that training collapses after approximately 150 steps when using NVFP4 with BF16 training. This collapse is accompanied by growing log-probability gaps between the rollout and the trainer, indicating a divergence in distribution representation. A detailed error analysis shows that the problem lies not in the weights but in the activations. Weights can be synchronized via a shared quantization-dequantization path, while activations are recomputed online and are subject to the coarse E2M1 grid of NVFP4, amplifying the error.
To address this instability, QUADS (Quantization-error Alignment across Dual Sides) has been proposed, a framework that aligns quantization error on both ends of the process: the trainer and the rollout generator. QUADS consists of two main components. First, on the trainer side, it introduces Asymmetric Quantization-Aware Training (Asymmetric QAT). Instead of simulating full model quantization, this technique quantizes only weights, leaving activations in full precision. This allows the trainer to learn representations aligned with the actual quantization that occurs during rollout inference, reducing discrepancy.
Second, on the rollout side, QUADS applies Residual Activation Compensation. During inference, activation channels with the highest quantization error are identified and a residual correction is applied, without modifying the native W4A4 GEMM operation. This correction is lightweight and does not affect overall throughput, yet stabilizes training by reducing accumulated activation error. Results on several MoE RL benchmarks show that QUADS achieves BF16-level accuracy, improves pass@1 by 21.49 points over naive NVFP4 RL, and delivers approximately 16% higher rollout throughput than FP8.
These advances are not only relevant for academic research but also have a direct impact on the development of artificial intelligence solutions in business environments. At Q2BSTUDIO, as a software development and technology company, we help organizations implement these optimized techniques in their infrastructures. Our experience in creating custom software applications allows us to adapt quantization algorithms to the specific needs of each client, whether to accelerate training of proprietary models or to deploy real-time inference systems.
The adoption of QUADS and formats like NVFP4 has implications across multiple areas. For instance, in cybersecurity, threat detection models can benefit from faster and more efficient inference, enabling responses to incidents in milliseconds. Q2BSTUDIO offers pentesting and cybersecurity services that integrate optimized models to protect critical infrastructures. Additionally, combining with Business Intelligence platforms like Power BI allows monitoring model performance in production, identifying bottlenecks and adjusting scaling strategies.
In cloud computing, QUADS reduces resource consumption on platforms like AWS or Azure. By requiring fewer high-precision operations, computational costs decrease significantly, which is especially important for startups and SMEs needing to scale without excessive budgets. Q2BSTUDIO provides specialized cloud services on AWS and Azure, helping deploy optimized infrastructures for AI workloads.
Another field of application is AI agents. Virtual assistants, chatbots, and autonomous systems require models that can generate responses quickly and adapt to changing contexts. The stability in RL training provided by QUADS is crucial for developing agents that learn consistently without collapsing. At Q2BSTUDIO we design custom intelligent agent solutions, integrating quantization techniques to achieve a balance between speed and accuracy.
It is important to note that implementing QUADS is not trivial. It requires deep knowledge of hardware architectures, such as GPUs that support native FP4, as well as implementation details of GEMM kernels. Companies wishing to adopt these technologies need a specialized team or a technology consulting partner. Q2BSTUDIO offers consulting and development services in AI, covering everything from quantization format selection to integration into training and deployment pipelines.
Furthermore, combining QUADS with other optimization techniques, such as model pruning or knowledge distillation, can generate important synergies. For example, a model quantized with NVFP4 and stabilized with QUADS can later be compressed through pruning to further reduce its memory footprint while maintaining accuracy. These strategies are part of Q2BSTUDIO's portfolio in process automation and custom software development.
In today's competitive landscape, the ability to efficiently train and deploy MoE models is a key differentiator. QUADS represents a significant advance in stabilizing low-precision RL, and its adoption can make the difference between an AI project that fails due to instability and one that achieves state-of-the-art results. At Q2BSTUDIO we are committed to technological innovation, offering solutions that range from initial consulting to ongoing maintenance of AI systems.
In conclusion, integrating QUADS into RL workflows for MoE not only solves the training collapse problem with NVFP4 but also paves the way for a new generation of faster and more efficient models. Companies investing in artificial intelligence should consider these techniques as part of their optimization strategy. With the support of partners like Q2BSTUDIO, it is possible to transform theory into practice, reducing costs and accelerating time-to-market. The intersection of advanced quantization, custom software development, and cloud services is where the future of AI is forged.
