GLM-4.1V-Thinking: Advanced Multimodal Reasoning

• GLM-4.1V-Thinking is a state-of-the-art vision-language model that integrates visual and textual data through reasoning-centric pre-training, fine-tuning, and RLCS.
• It employs a specialized architecture with a Vision Transformer, MLP adapter, and language decoder to support flexible image/video inputs and explicit temporal modeling.
• It achieves robust performance across 28 benchmarks in domains like STEM, video understanding, coding, and document analysis, serving as a reproducible baseline for research.

GLM-4.1V-Thinking denotes a general-purpose vision-LLM (VLM) with advanced multimodal reasoning capabilities, open-sourced as GLM-4.1V-9B-Thinking. It was developed via reasoning-centric large-scale pre-training, supervised fine-tuning, and scalable reinforcement learning with curriculum sampling (RLCS). The model advances the state of the art among models of comparable size, demonstrating robust performance across STEM, video, content recognition, coding, grounding, GUI agents, and long document understanding tasks. Its design and training emphasize explicit reasoning and broad cross-domain generalization.

1. Model Architecture and Capabilities

GLM-4.1V-Thinking comprises three main architectural components:

• Vision Encoder: AIMv2-Huge, a Vision Transformer (ViT), extracts visual embeddings from both static images and video sequences.
• MLP Adapter/Projector: Projects visual features into the embedding space of the LLM, aligning modalities for joint processing.
• Language Decoder: Utilizes the GLM LLM to process joint multimodal sequences and generate outputs.

Key Features in Model Design

• Temporal Modeling: 3D convolutions replace standard 2D convolutions in the vision encoder, enabling effective temporal downsampling (factor of two) for video. Each frame is accompanied by a time index token to enhance temporal context for the LLM.
• Flexible Input Handling: The model accommodates arbitrary image resolutions and aspect ratios (including >4K and >200:1) by employing 2D rotary position embedding (2D-RoPE) and bicubic interpolation of absolute position embeddings:

  $$\mathbf{g}_{\mathrm{norm}} = (w_{\mathrm{norm}}, h_{\mathrm{norm}}) = 2 \cdot \left( \frac{w+0.5}{W_p}, \frac{h+0.5}{H_p} \right) - 1 \ P_{\mathrm{adapted}}(\mathbf{g}) = \mathcal{I}_{\mathrm{bicubic}}(P_{\mathrm{orig}}, \mathbf{g}_{\mathrm{norm}})$$

• 3D Rotary Position Embedding in LLM: RoPE is extended to three dimensions in the decoder to integrate spatial and temporal cues, vital for multimodal reasoning involving sequences such as video.
• Input Modality: Both images and videos are processed at native aspect ratio and resolution, supporting intra- and inter-frame reasoning with explicit temporal indexing.

2. Training Methodology

The training pipeline is structured to maximize reasoning ability via a sequential, multi-stage approach:

2.1 Multimodal Pre-training

• Dataset Curation: Billions of high-quality image-text pairs, curated for factual accuracy, and diverse sources combining natural data (webpages, scanned documents, books) and synthetic content (OCR, math, charts, GUI screens).
• Balanced Sampling and Concept Coverage: Systematic filtering and deduplication regulate class and task balance for broad concept coverage.
• Temporal Event Data: Annotated video datasets with temporal/event-level annotations enable robust video understanding.

2.2 Supervised Fine-Tuning (SFT)

• Long Chain-of-Thought (CoT) Dataset: Fine-tuning employs a multi-stage, high-quality CoT corpus with strict chain-of-thought reasoning format:

  <think> ...reasoning steps... </think> <answer> ...final answer... </answer>

  For verifiable outputs, a markup (box) token marks the extractable answer, ensuring robust support for programmatic evaluation.
• Data Quality Assurance: Only high-quality, logically correct examples are retained. Iterative cleaning improves downstream reinforcement learning (RL) stability.

2.3 Scalable Reinforcement Learning with Curriculum Sampling (RLCS)

• Reward System:
  • RLVR (Reinforcement Learning with Verifiable Rewards): Rewards are based on correct final answers, using symbolic/exact/semantic matching (e.g., sympy for math, IoU for grounding, LLM-based for open-ended items). The framework avoids reward hacking with explicit answer markers.
  • RLHF: In subjective or non-verifiable domains, alignment is guided by model- or human-based reward models.
• Curriculum Sampling:
  • Difficulty Grading: Offline grading (using pass@k from various models or manual labels) combines with online performance tracking to dynamically adjust the sampling of easy, medium, and hard tasks, up-weighting high-information samples.
  • Dynamic Batch Management: Exponential Moving Average (EMA) batch expansion keeps training efficient even as tasks become easier.
  • Forced Answer Enforcement: For overlong CoTs, answer markers are injected to train shorter and more focused outputs.
• Infrastructure: Batched, parallel, and variable-length sequence RL enables efficient RLCS domain mixing, with large-scale hardware and software support.

3. Empirical Performance and Comparative Results

GLM-4.1V-9B-Thinking demonstrates strong, often state-of-the-art, results across a comprehensive battery of 28 public benchmarks that span multiple domains:

Domain/Task	Example Benchmarks	Notable Metrics	Performance Summary
General Visual QA	MMBench-V1.1, MMStar, ...	Accuracy	85.8 on MMBench-V1.1-EN (beats larger open models)
STEM (Science & Math)	MMMU, AI2D, MathVista, ...	Accuracy, Pass@k	57.1 on MMMU-Pro (vs. 38.3, 51.1 for comparison)
Charts & OCR	ChartQAPro, ChartMuseum	QA/MCQ accuracy	48.8 on ChartMuseum (vs. 27.2, 39.6 for comparison)
Long Document QA	MMLongBench-Doc	QA accuracy	42.4 (exceeding GPT-4o’s 41.0, open-source SoTA)
Video Understanding	VideoMME, LVBench, ...	Video Q/A	Maintains robust video comprehension
Grounding / Agents	RefCOCO, OSWorld, WebQuest	Localization / Step accuracy	Outperforms on GUI- and agent-based benchmarks
Coding	Flame-VLM-Code, Design2Code	Syntactic/sem. correctness	72.5 (vs. 46.3 on comparable-sized open models)

• GLM-4.1V-9B-Thinking outperforms Qwen2.5-VL-7B (7B) on nearly all tasks, and rivals or exceeds Qwen2.5-VL-72B (72B) on 18/28 evaluations, despite being just 1/8 the size.
• On MMLongBench-Doc, it surpasses proprietary GPT-4o.

A plausible implication is that performance gains achieved via reinforcement learning with curriculum sampling not only improve the targeted task but also transfer across domains (e.g., STEM RL benefits general VQA and agentic tasks), suggesting cross-domain generalization in reasoning-centric multimodal models.

4. Applications and Use Cases

The model is deployed in a wide array of multimodal contexts:

• Video Analysis and Reasoning: Gives temporally structured, accurate narration and answers to open-ended video prompts.
• STEM Instruction and Math Problem Solving: Handles document math, equation diagrams, logic puzzles, and complex multi-step solutions.
• Document and Content Extraction: Reads and interprets documents of arbitrary length and aspect ratio, supporting accurate OCR and knowledge synthesis.
• Chart and Data Visualization: Answers quantitative and logical questions about plots, bar charts, and scientific diagrams.
• GUI-based Agency: Interprets and manipulates software and web interfaces, supporting agent workflows for automated UI navigation and reasoning.
• Code-Related Vision Tasks: Generates and debugs code based on screenshots or UIs, assisting front-end development.

These applications are supported with domain-specific reward modules (e.g., symbolic math solvers, image region matching, semantic answer matching) for robust self-improvement in RL training.

5. Open Source Contribution and Benchmark Impact

GLM-4.1V-9B-Thinking is fully open-sourced, including code, pretrained models, RL checkpoints, and reward extraction modules (https://github.com/THUDM/GLM-4.1V-Thinking). This provides a uniquely accessible, high-performing, and reproducible baseline for academic and industrial multimodal reasoning research. Researchers can directly build upon, extend, or adapt the foundation for further advances or domain transfer experiments.

The release of benchmark results across 28 tasks provides a transparent comparison point against both open-source and proprietary systems, enabling informed assessment of progress in multimodal reasoning capabilities.

6. Limitations and Future Directions

• Reward Modeling for Reasoning Chains: RLVR currently provides feedback only on final answers, not intermediate reasoning. As a result, invalid or hallucinated reasoning steps can be reinforced if end output is correct. The development of reward models evaluating stepwise logical correctness is a proposed research avenue.
• RL Training Stability: The system is sensitive in early RL phases, where minor pipeline changes can induce instability and large performance swings. Research into more robust and consistent RL algorithms for multimodal models is needed.
• Perceptual-Reasoning Synergy: Remaining weaknesses include handling of cluttered scenes, occlusion, and ambiguity, suggesting further integration between perception modules and symbolic reasoning steps.
• Benchmark Saturation: Many multimodal benchmarks are near-saturated among leading models, making it difficult to distinguish shortcutting or hallucination. The design of more diagnostic, failure-targeted multimodal reasoning challenges is needed.

A plausible implication is that addressing these limitations may catalyze advances in both the fidelity and transparency of multimodal reasoning systems, further bridging the gap between human and AI reasoning in open-ended vision-language domains.

7. Conclusion

GLM-4.1V-Thinking represents a significant advancement in sub-10B parameter vision-LLMs, combining high-quality multimodal pre-training, carefully filtered chain-of-thought supervision, and large-scale RL with curriculum sampling. Its open-source availability and superior or competitive evaluation on a diverse range of challenging benchmarks establish it as a state-of-the-art foundation for future research and industrial deployment in multimodal reasoning.