Visual Quality Assessment (QA) in multimodal large language models (MLLMs) jointly predicts absolute quality scores and relative perceptual rankings. Existing methods optimize score regression or ranking within a unified formulation, leading to unstable predictions and weak generalization under stochastic generation. We find this instability is amplified by response variability in MLLM reasoning, where stochastic decoding produces inconsistent score distributions and degrades cross-dataset robustness. This indicates QA depends not only on preference alignment, but also on response-level stability. We propose PreResQ-R1, a Response-Preference Disentangled Ranking-and-Scoring Reinforcement Optimization framework. It decouples response stabilization and preference learning, with a dual-branch reward modeling intra-sample consistency and inter-sample score-ranking alignment. For video QA, we introduce temporal-spatial evidence aggregation without dense motion modeling. PreResQ-R1 achieves state-of-the-art performance, outperforming comparable methods by 5.60% on IQA benchmarks and 2.53% on VQA benchmarks.

Heavily overparameterized language models such as BERT, XLNet and T5 have achieved impressive success in many NLP tasks. However, their high model complexity requires enormous computation resources and extremely long training time for both pre-training and fine-tuning. Many works have studied model compression on large NLP models, but only focusing on reducing inference time while still requiring an expensive training process. Other works use extremely large batch sizes to shorten the pre-training time, at the expense of higher computational resource demands. In this paper, inspired by the Early-Bird Lottery Tickets recently studied for computer vision tasks, we propose EarlyBERT, a general computationally-efficient training algorithm applicable to both pre-training and fine-tuning of large-scale language models. By slimming the self-attention and fully-connected sub-layers inside a transformer, we are the first to identify structured winning tickets in the early stage of BERT training. We apply those tickets towards efficient BERT training, and conduct comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks. Our results show that EarlyBERT achieves comparable performance to standard BERT, with 35 45% less training time. Code is available at https://github.com/VITA-Group/EarlyBERT.