Visual Language Post-Training Strategies

• Visual Language Post-Training is a set of adaptation strategies that optimize LVLMs and MLLMs after generic pre-training, enhancing visual reasoning and domain-specific task performance.
• The approach integrates selective layer tuning, domain-adaptive pipelines, reinforcement learning, self-supervised tasks, and quantization to achieve up to 99% performance retention with reduced computation.
• These methods improve instruction-following, efficiency in resource-constrained deployments, and robust multi-modal alignment, offering measurable gains on benchmarks like COCO mAP and inference speed.

Visual Language Post-Training is the set of adaptation strategies, optimization protocols, and architectural modifications applied to large vision-LLMs (LVLMs) and multimodal LLMs (MLLMs) after pre-training on generic image-text corpora. These post-training methodologies enable efficient and robust injection of visual reasoning, domain adaptation, instruction-following, multi-modal alignment, resource-efficient deployment, and specialized task capabilities. Techniques span selective layer tuning, supervised and reinforcement learning, self-supervised vision-centric tasks, quantization, curriculum approaches, and architectural fusion. Recent advances have formalized the identification of neural "visual regions," established unified optimization objectives blending external and internal signals, and designed post-training pipelines for models at scale.

1. Selective Layer Tuning and Visual Region Identification

Recent evidence demonstrates that visual reasoning within LVLMs can be efficiently acquired via selective tuning of a small, distributed subset of transformer layers—termed the "visual region" (Wang et al., 2024). In this approach, only $k = \alpha L$ layers (typically $\alpha = 0.25$) are tuned, selected by sparse uniform spacing:

$$S = \{ \lfloor i \cdot L / k \rfloor \mid i = 0,\ldots,k-1 \}$$

This uniformly distributed region consistently outperforms alternatives (e.g., Block Influence Score, parameter change ratio, angular distance) for both visual perception and cognition benchmarks.

Selective layer tuning involves:

• Freezing all layers outside $S$.
• Inserting low-rank adapters (LoRA) with parameters at $S$.
• Minimizing instruction-following cross-entropy plus regularization:

$$L(\theta_S) = \frac{1}{N} \sum_{n=1}^N CE(y_n, \hat{y}_n; \theta_S) + \lambda \|\theta_S\|_2^2$$

Empirical results indicate that updating only 25% of layers preserves $\sim$99% of visual performance and improves language task retention, at 75% reduced parameter-update cost. Region-guided post-training achieves 12-23% reduction in GPU·hours across models and 10% inference speedup via targeted pruning of noncritical layers not in $S$.

2. Domain-Adaptive Post-Training

Task-specific domain performance in MLLMs is optimally achieved via post-training pipelines tailored to expert data (Cheng et al., 2024). The "generate-then-filter" approach synthesizes high-diversity, domain-specific visual instruction tasks by:

• Fine-tuning open-source MLLMs on curated image–caption datasets.
• Generating instructions and responses, with 10% caption-only blanking.
• Filtering synthetic tasks for consistency, merging into chain-of-thought (CoT) outputs.

Single-stage mixing of captioning and instruction tasks prevents catastrophic forgetting observed in traditional two-stage pipelines. On food/biomedicine, such single-stage post-training yields superior accuracy (average +3–15 absolute points) and higher complexity/knowledge coverage than rule-based or closed-source pipelines, with robust cross-domain generalization.

3. Reinforcement Learning and Human-Free Alignment

Vision-guided RL post-training methods, such as Vision-R1 (Zhan et al., 23 Mar 2025), employ rule-based rewards that leverage definitive vision feedback (e.g., bounding box IoU, format correctness). These approaches use Group Relative Policy Optimization (GRPO) with criterion-driven rewards:

$$R(o) = r_{DF}(o) + r_{\text{recall}}(o) + r_{\text{prec}}(o)$$

Progressive rule refinement tightens reward criteria during training, mitigating reward hacking and enabling continuous improvement.

Vision-R1 achieves mAP gains of up to +8.9 on COCO and ODINW benchmarks (surpassing much larger models), and demonstrates robustness on both in-distribution and out-of-distribution tasks. Preference annotation and reward model training are entirely eliminated, lowering alignment cost and increasing scalability.

4. Unified Supervised-and-Reinforcement Fine-Tuning

Consensus is emerging around the integration of supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR). ViSurf (Liu et al., 12 Oct 2025) introduces a unified objective:

• Ground-truth labels are injected as special rollouts in the RL batch.
• Standardized advantages are computed across both SFT and RLVR candidates.
• Training dynamically balances external supervision and internal reinforcement:

$$\mathcal{L}_\text{ViSurf}(\theta) = -\mathbb{E} \left[ \frac{1}{G+1} \left(\sum_{j=1}^G \hat{A}_j \nabla_\theta \log \pi_\theta(o_j) + \hat{A}_y \nabla_\theta \log \pi_\theta(y) \right) \right]$$

Reward control strategies—such as ground-truth alignment, elimination of reasoning format reward, and adaptive smoothing—are critical for training stability. ViSurf outperforms independent SFT, RLVR, and sequential SFT→RLVR protocols across non-object segmentation, GUI grounding, reasoning QA, anomaly detection, visual math, and multi-domain tasks.

5. Vision-Centric and Self-Supervised Post-Training

Text-centric paradigms can insufficiently leverage dense visual signals. Vision-centric post-training methods—such as Visual Jigsaw (Wu et al., 29 Sep 2025)—induce intrinsic visual understanding by presenting shuffled partitions of images, videos, or 3D point sets. RLVR trains the model to reconstruct the original permutation in natural language, employing self-supervised, annotation-free rewards:

$$\text{Reward}(o, \pi^*) = \begin{cases} 1, & o = \pi^* \ \gamma \frac{1}{K} \sum_{i=1}^K \mathbf{1}[o_i = \pi_i^*], & \text{ValidPerm}(o) \land o \neq \pi^* \ 0, & \text{otherwise} \end{cases}$$

This paradigm yields substantial improvement in fine-grained perception (up to +6% on MMStar), temporal reasoning, and 3D spatial tasks, with generality across modalities and architectures.

6. Instruction-Following and Catastrophic Forgetting

Post-training on multimodal data can erode LLMs’ intrinsic instruction-following capacity, especially with respect to output formatting (Shiono et al., 29 Dec 2025). Explicit incorporation of output-format instructions during visual instruction tuning, even at low frequency (3% of data), restores adherence to prompt structure, schema, and verbalizer requirements. Verbalizer-manipulation evaluation protocols highlight this effect: LVLMs with format-oriented data outperform both standard-tuned LVLMs and text-only LLMs on token-level F1 and exact match metrics, while maintaining visual comprehension.

7. Quantization and Efficiency in Resource-Constrained Deployment

Post-training quantization (PTQ) using cross-layer dependency mining, as in Q-VLM (Wang et al., 2024), enables high-compression, low-latency inference for LVLMs:

• Activation entropy serves as a proxy for discretization error coupling across layers.
• Block-wise quantization optimally partitions the network, further refined by disentangling the visual encoder via auxiliary objectives.
• Empirical results: 2.78× memory reduction, 1.44× speed increase at negligible accuracy loss over ScienceQA, VizWiz, VQA v2, and Hateful Memes.

8. Long-Context and Spatiotemporal Adaptation

Methods such as Eagle 2.5 (Chen et al., 21 Apr 2025) post-train LVLMs to handle extreme context lengths and long video/image sequences. Automatic Degrade Sampling (ADS) and Image Area Preservation (IAP) dynamically allocate sequence tokens, preserving textual integrity and maximizing image area within compute constraints. Progressive mixed post-training (gradually increasing max sequence length) improves performance on long-context benchmarks (MVBench, MLVU, Video-MME) and robustifies inference for multi-minute and high-resolution content.

9. Taxonomic Knowledge, Task Deployment, and Behavioral Implications

Vision-language post-training does not alter the fundamental taxonomic organization of pretrained LLMs, but improves deployment of such knowledge on downstream tasks (Qin et al., 17 Jul 2025). VLMs outperform text-only LMs on purely textual QA tasks requiring hierarchical reasoning, attributable to sharper contextual representation and routing rather than static knowledge change. Representational similarity analysis and logistic regression on contextual similarities confirm enhanced dynamic access to lexical hierarchies post VL tuning.

10. Vision-Language-Action (VLA) Models: Taxonomy and Parallels to Human Motor Learning

Post-training for VLA models encompasses adaptation of vision, language, and action heads toward specific environment, embodiment, and task demands. The four-category taxonomy (Xiang et al., 26 Jun 2025):

• Environmental perception (affordance learning, encoder distillation, representation alignment).
• Embodiment awareness (forward/inverse kinematic modeling, action head design).
• Task comprehension (human-robot interaction, hierarchical planning).
• Multi-component integration (combined RL/BC, video prediction, active dataset processing).

Empirical results show 5–60% improvements in task success and 2–5× sample efficiency gains. This adaptation is structurally aligned with staged human motor skill acquisition and emphasizes the need for continual learning, curriculum design, multimodal integration, and explainable decision-making.

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In sum, visual language post-training comprises selective architectural adaptations, hybrid learning objectives, self-supervised and vision-centric pipelines, efficiency optimizations, and instruction-following preservation mechanisms. These advances collectively enable LVLMs and MLLMs to achieve state-of-the-art capability across perception, reasoning, alignment, efficiency, and specialized task domains—while overcoming catastrophic forgetting and scaling to frontier application contexts (Wang et al., 2024, Cheng et al., 2024, Zhan et al., 23 Mar 2025, Liu et al., 12 Oct 2025, Wu et al., 29 Sep 2025, Shiono et al., 29 Dec 2025, Wang et al., 2024, Chen et al., 21 Apr 2025, Qin et al., 17 Jul 2025, Xiang et al., 26 Jun 2025).