Second-Order Multi-Level Variance Correction for Modality Competition in Multimodal Models

• URL: http://arxiv.org/abs/2605.16165v1
• Date: Fri, 15 May 2026 16:45:56 GMT
• Title: Second-Order Multi-Level Variance Correction for Modality Competition in Multimodal Models
• Abstract summary: We show that first-order preconditioning, particularly SOAP, provides a more stable basis for multimodal alignment.<n>We propose emphML-FOP-SOAP, a second-order optimization framework with Multi-Level Variance Correction.<n>Compared with AdamW, our method improves sample efficiency by up to $1.4times$ and accelerates wall-clock training by up to $1.5times$.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive next-token training offers a unified formulation for image generation and text understanding, but it also creates strong modality competition that destabilizes optimization and limits large-batch scaling. We show that first-order optimizers such as AdamW are vulnerable to cross-modality gradient heterogeneity, while second-order preconditioning, particularly SOAP, provides a more stable basis for multimodal alignment. Building on this insight, we propose \emph{ML-FOP-SOAP}, a second-order optimization framework with Multi-Level Variance Correction. Our Fisher-Orthogonal Projection suppresses variance-induced modality conflicts, reducing the trade-off between visual generation and textual understanding. To make this practical under large gradient accumulation, we introduce a hierarchical folding strategy that captures fine-grained variance with low micro-step overhead. Experiments on Janus and Emu3 show consistent gains across both modalities and stable training at batch size 8192. Compared with AdamW, our method improves sample efficiency by up to $1.4\times$ and accelerates wall-clock training by up to $1.5\times$, offering a robust optimizer for scaling multimodal foundation models.

Related papers

• AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters [67.51886052453452]
  Speculative decoding has become a prevalent paradigm for accelerating large language model inference.<n>Recent work such as DFlash further boosts drafting efficiency by leveraging diffusion drafters.<n>We propose AdaFlash framework, comprising two components: (i) an on-policy distillation (OPD) algorithm with reverse-KL divergence tailored for diffusion drafters, and (ii) an adaptive length head that dynamically adjusts the candidate sequence length on the fly.
  arXiv  Detail & Related papers  (2026-07-21T15:52:50Z)
• Policy-based Tuning of Autoregressive Image Models with Instance- and Distribution-Level Rewards [16.135177543347773]
  Autoregressive (AR) models are highly effective for image generation, yet their standard maximum-likelihood estimation training lacks direct optimization for sample quality and diversity.<n>We propose a lightweight RL framework that casts token-based AR inference as a Markov Decision Process, optimized via Group Relative Policy Optimization.<n>Our core contribution is the introduction of a novel distribution-level Leave-One-Out FID (LOO-FID) reward.
  arXiv  Detail & Related papers  (2026-03-24T11:28:36Z)
• Trajectory Consistency for One-Step Generation on Euler Mean Flows [24.038760671907024]
  We propose emphEuler Mean Flows (EMF), a flow-based generative framework for one-step and few-step generation.<n>EMF enforces long-range trajectory consistency with minimal sampling cost.
  arXiv  Detail & Related papers  (2026-01-31T04:32:32Z)
• Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model [62.889356203346985]
  We propose DUal-STream diffusion (DUST), a world-model augmented VLA framework that handles the modality conflict.<n>DUST achieves up to 6% gains over a standard VLA baseline and implicit world-modeling methods.<n>On real-world tasks with the Franka Research 3, DUST outperforms baselines in success rate by 13%.
  arXiv  Detail & Related papers  (2025-10-31T16:32:12Z)
• SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion [0.8594140167290097]
  SPREAD is a generative framework based on Denoising Diffusion Probabilistic Models (DDPMs)<n>It learns a conditional diffusion process over points sampled from the decision space.<n>It refines candidates via a sampling scheme that uses an adaptive multiple gradient descent-inspired update for fast convergence.
  arXiv  Detail & Related papers  (2025-09-25T12:09:37Z)
• Improving Progressive Generation with Decomposable Flow Matching [50.63174319509629]
  Decomposable Flow Matching (DFM) is a simple and effective framework for the progressive generation of visual media.<n>On Imagenet-1k 512px, DFM achieves 35.2% improvements in FDD scores over the base architecture and 26.4% over the best-performing baseline.
  arXiv  Detail & Related papers  (2025-06-24T17:58:02Z)
• Layer-wise Quantization for Quantized Optimistic Dual Averaging [75.4148236967503]
  We develop a general layer-wise quantization framework with tight variance and code-length bounds, adapting to the heterogeneities over the course of training.<n>We propose a novel Quantized Optimistic Dual Averaging (QODA) algorithm with adaptive learning rates, which achieves competitive convergence rates for monotone VIs.
  arXiv  Detail & Related papers  (2025-05-20T13:53:58Z)
• VAMO: Efficient Zeroth-Order Variance Reduction for SGD with Faster Convergence [6.574641780732972]
  Large-scale non problems are common in deep learning.<n>First-order (FO)s serve as today's baselines.<n>ZO algorithms reduce the size of computation and memory costs.<n>VAMO achieves these gains with smaller dynamic memory requirements.
  arXiv  Detail & Related papers  (2025-05-20T05:31:15Z)
• Flow-GRPO: Training Flow Matching Models via Online RL [80.62659379624867]
  We propose Flow-GRPO, the first method to integrate online policy reinforcement learning into flow matching models.<n>Our approach uses two key strategies: (1) an ODE-to-SDE conversion that transforms a deterministic Ordinary Differential Equation into an equivalent Differential Equation (SDE) that matches the original model's marginal distribution at all timesteps; and (2) a Denoising Reduction strategy that reduces training denoising steps while retaining the original number of inference steps.
  arXiv  Detail & Related papers  (2025-05-08T17:58:45Z)
• Taming Flow Matching with Unbalanced Optimal Transport into Fast Pansharpening [10.23957420290553]
  We propose the Optimal Transport Flow Matching framework to achieve one-step, high-quality pansharpening.<n>The OTFM framework enables simulation-free training and single-step inference while maintaining strict adherence to pansharpening constraints.
  arXiv  Detail & Related papers  (2025-03-19T08:10:49Z)
• MARS: Unleashing the Power of Variance Reduction for Training Large Models [56.67982828148859]
  We propose a unified training framework for deep neural networks.<n>We introduce three instances of MARS that leverage preconditioned gradient optimization.<n>Results indicate that the implementation of MARS consistently outperforms Adam.
  arXiv  Detail & Related papers  (2024-11-15T18:57:39Z)
• Adaptive Federated Learning Over the Air [108.62635460744109]
  We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. Our analysis shows that the AdaGrad-based training algorithm converges to a stationary point at the rate of $mathcalO( ln(T) / T 1 - frac1alpha ).
  arXiv  Detail & Related papers  (2024-03-11T09:10:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.