Neural Information Processing Systems http://nips.cc/

ZIP Lab, Monash University, Australia We organize our supplementary material as follows: In section A, we provide a comprehensive explanation of extending PTQD to DDIM [10]. In section B, we show the statistical analysis of quantization noise. In section D, we provide additional visualization results on ImageNet and LSUN dataset. We first perform statistical tests to verify if the residual quantization noise adheres to a Gaussian distribution. This test is based on D'Agostino and Pearson's In Figure B, we present the variance of the residual uncorrelated quantization noise.

Diffusion models have recently dominated image synthesis and other related generative tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models less practical for low-latency and scalable real-world applications.

Diffusion models have recently dominated image synthesis and other related generative tasks. However, the iterative denoising process is expensive in computations at inference time, making diffusion models less practical for low-latency and scalable real-world applications. Post-training quantization of diffusion models can significantly reduce the model size and accelerate the sampling process without requiring any re-training. Nonetheless, applying existing post-training quantization methods directly to low-bit diffusion models can significantly impair the quality of generated samples. Specifically, for each denoising step, quantization noise leads to deviations in the estimated mean and mismatches with the predetermined variance schedule. Moreover, as the sampling process proceeds, the quantization noise may accumulate, resulting in a low signal-to-noise ratio (SNR) during the later denoising steps. To address these challenges, we propose a unified formulation for the quantization noise and diffusion perturbed noise in the quantized denoising process.

Sensing is the process of deriving signals from the environment that allows artificial systems to interact with the physical world. The Shannon theorem specifies the maximum rate at which information can be acquired [1]. However, this upper bound is hard to achieve in many man-made systems. The biological visual systems, on the other hand, have highly efficient signal representation and processing mechanisms that allow precise sensing. In this work, we argue that redundancy is one of the critical characteristics for such superior performance.

We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-intensive, requiring substantial GPU memory and long rollout durations. Beyond efficiency, our findings show that quantization noise increases policy entropy, enhancing exploration, and enabling the discovery of better strategies during RL. To further optimize exploration, QeRL introduces an Adaptive Quantization Noise (AQN) mechanism, which dynamically adjusts noise during training. Experiments demonstrate that QeRL delivers over 1.5 speedup in the rollout phase. Moreover, this is the first framework to enable RL training of a 32B LLM on a single H100 80GB GPU, while delivering overall speedups for RL training. It also achieves faster reward growth and higher final accuracy than 16-bit LoRA and QLoRA, while matching the performance of full-parameter fine-tuning on mathematical benchmarks such as GSM8K (90.8%) and MA TH 500 (77.4%) in the 7B model. These results establish QeRL as an efficient and effective framework for RL training in LLMs.Figure 1: Rollout speedup and accuracy of QeRL on Qwen2.5-7B-Instruct. QeRL achieves faster RL rollout and end-to-end training speeds (batch=8), while delivering performance superior to vanilla LoRA and QLoRA, also comparable to full-parameter RL on mathematical benchmarks. The ability to perform multi-step reasoning is critical for large language models (LLMs) to handle complex tasks, from theoretical problem solving to practical decision making (Sui et al., 2025; Xu et al., 2025; Chu et al., 2025; Y ang et al., 2021). Supervised fine-tuning (SFT) is a common method to improve reasoning by training models to replicate explicit reasoning steps (Huang et al., 2024d; Min et al., 2024). In contrast, reinforcement learning (RL) uses verifiable reward signals to support adaptive learning, allowing models to explore diverse reasoning traces and identify more robust solutions (Lambert et al., 2024; DeepSeek-AI, 2025; Chen et al., 2025a). 1 AQN dynamically adjusts quantization noise with an exponential scheduler, enhancing exploration.

ZIP Lab, Monash University, Australia We organize our supplementary material as follows: In section A, we provide a comprehensive explanation of extending PTQD to DDIM [10]. In section B, we show the statistical analysis of quantization noise. In section D, we provide additional visualization results on ImageNet and LSUN dataset. We first perform statistical tests to verify if the residual quantization noise adheres to a Gaussian distribution. This test is based on D'Agostino and Pearson's In Figure B, we present the variance of the residual uncorrelated quantization noise.

Convolutional neural networks require significant memory bandwidth and storage for intermediate computations, apart from substantial computing resources. Neural network quantization has significant benefits in reducing the amount of intermediate results, but it often requires the full datasets and time-consuming fine tuning to recover the accuracy lost after quantization. This paper introduces the first practical 4-bit post training quantization approach: it does not involve training the quantized model (fine-tuning), nor it requires the availability of the full dataset. We target the quantization of both activations and weights and suggest three complementary methods for minimizing quantization error at the tensor level, two of whom obtain a closed-form analytical solution. Combining these methods, our approach achieves accuracy that is just a few percents less the state-of-the-art baseline across a wide range of convolutional models.