ReRound: Reconstructive Rounding to Resolve Midpoint Ambiguity in Calibration-Free LLM Quantization

Analysis

Why This Paper Matters

Quantization is a critical technique for deploying large language models (LLMs) on resource-constrained devices. Traditional round-to-nearest (RTN) quantization suffers from a fundamental ambiguity when a weight falls exactly at the midpoint of a quantization interval—any rounding direction is equally valid, yet the choice can significantly impact model accuracy. Calibration-based methods mitigate this by using data to adjust rounding, but they require access to representative datasets, which may be unavailable or privacy-sensitive. ReRound addresses this gap by offering a calibration-free method that leverages a conditional diffusion model to resolve midpoint ambiguity, achieving accuracy competitive with calibration-dependent approaches.

The significance of ReRound lies in its novel use of generative models for quantization. Instead of relying on data statistics, it learns to reconstruct low-bit weights from the pretrained model itself, providing a data-free guidance signal. This opens a new avenue for post-training quantization, where the model's own knowledge is harnessed to make better rounding decisions. The method is particularly effective for smaller LLMs, which are common in edge deployments, making it a practical contribution to efficient AI.

Technical Contributions

• Diffusion-guided rounding: ReRound trains a conditional diffusion model to generate continuous reconstructions of low-bit weights. These reconstructions serve as a guidance signal to decide the rounding direction for weights near interval midpoints, resolving the ambiguity that RTN ignores.
• Tolerance metric: A novel tolerance metric quantifies the distance of a quantized weight from the midpoint. Weights within a tolerance region are quantized using diffusion-based reconstructions, while weights near boundaries use RTN, ensuring that only ambiguous cases are handled by the diffusion model.
• Candidate selection via singular values: By sweeping the tolerance parameter, ReRound generates multiple candidate quantized weight matrices. It selects the candidate whose de-quantized weights have leading singular values most similar to the original full-precision weights, providing a principled way to choose the tolerance without any calibration data.
• Offline operation: The entire process is offline, meaning no additional overhead during inference, which is crucial for deployment.

Results

The paper reports that ReRound consistently outperforms standard RTN for 3-bit and 4-bit weight quantization across a range of small LLMs. It achieves superior accuracy compared to an extensive set of calibration-free methods and remains competitive with calibration-dependent approaches. While specific numerical metrics are not provided in the abstract, the qualitative claims indicate a clear improvement over RTN and other calibration-free baselines, with performance close to methods that require calibration data.

Significance

ReRound's approach of using diffusion models for quantization is a novel contribution that could inspire further research into generative model-based compression techniques. By eliminating the need for calibration data, it makes quantization more accessible and privacy-preserving. The method's applicability to AI models beyond LLMs suggests broad potential impact. However, the focus on small LLMs leaves open questions about scalability to larger models, which future work may address. Overall, ReRound represents a meaningful step forward in calibration-free quantization, with practical implications for edge AI and model deployment.