Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: text-generation
+---
+
+# Innovator-VL-8B-Thinking
+
+## Introduction
+
+**Innovator-VL-8B-Thinking** is a multimodal reasoning-oriented large
+language model designed for complex scientific problem solving. Built
+upon Innovator-VL-8B-Instruct, this model is further optimized for
+explicit multi-step reasoning, long-horizon chain-of-thought generation,
+and token-efficient scientific analysis.
+
+The model is particularly suitable for scientific tasks that require
+structured reasoning over visual and textual evidence, such as
+mathematics, chemistry, materials science, and multimodal scientific
+benchmarks.
+
+------------------------------------------------------------------------
+
+## Model Overview
+
+-   **Model Type**: Vision-Language Reasoning Model
+-   **Parameter Size**: 8B
+-   **Base Language Model**: Qwen3-8B-Base
+-   **Vision Encoder**: RICE-ViT
+-   **Projector**: PatchMerger
+
+The model supports native-resolution multi-image inputs and is optimized
+for reasoning-intensive multimodal scenarios.
+
+------------------------------------------------------------------------
+
+## Key Characteristics
+
+### Explicit Multimodal Reasoning
+
+Innovator-VL-8B-Thinking is trained to explicitly generate structured
+reasoning traces, enabling the model to: - Perform multi-step logical
+deduction grounded in visual evidence - Solve complex mathematical and
+scientific problems - Maintain reasoning consistency across long
+contexts
+
+### Reinforcement Learning for Long-Horizon Reasoning
+
+The model is further optimized using reinforcement learning to
+improve: - Reasoning correctness - Output consistency - Token efficiency
+in long chain-of-thought generation
+
+Sequence-level optimization enables strong accuracy while significantly
+reducing unnecessary reasoning tokens.
+
+### Scientific Reasoning Performance
+
+Compared to instruction-only models, Innovator-VL-8B-Thinking
+demonstrates substantial gains on: - Multimodal mathematical reasoning
+benchmarks - Scientific reasoning and domain-specific QA - Tasks
+requiring precise step-by-step analysis
+
+------------------------------------------------------------------------
+
+## Model Architecture
+
+`<img src="assets/innovator_vl_architecture.png" width="600"/>`{=html}
+
+-   **Vision Encoder**: RICE-ViT (region-aware visual representation)
+-   **Projector**: PatchMerger for visual token compression
+-   **Language Model**: Qwen3-8B-Base
+-   **Model Size**: 8B parameters
+
+The architecture is shared with the Instruct variant, while the
+optimization objective and training strategy differ at the post-training
+stage.
+
+------------------------------------------------------------------------
+
+## Training Pipeline
+
+### Multimodal Pre-training
+
+-   Vision-language alignment with LLaVA-1.5 (558K)
+-   Full-parameter mid-training using LLaVA-OneVision-1.5 (85M)
+
+### Instruction Initialization
+
+-   Initialized from Innovator-VL-8B-Instruct
+-   Supervised fine-tuning with multimodal instruction and reasoning
+    data
+
+### Reinforcement Learning
+
+-   Trained with Innovator-VL-RL-172K
+-   Optimized using Group Sequence Policy Optimization (GSPO)
+-   Reward design jointly considers reasoning structure and answer
+    correctness
+
+------------------------------------------------------------------------
+
+## Usage Recommendations
+
+This model is recommended for: - Multimodal mathematical reasoning -
+Scientific problem solving requiring explicit reasoning - Evaluation
+settings emphasizing chain-of-thought quality
+
+For general instruction-following or latency-sensitive applications, the
+Instruct version is recommended.
+
+------------------------------------------------------------------------
+
+## Citation
+
+@article{innovator-vl, title={Innovator-VL: A Multimodal Large Language
+Model for Scientific Discovery}, year={2025} }