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.

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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.

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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

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Model Architecture

• 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.

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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

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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.

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Citation

@article{innovator-vl, title={Innovator-VL: A Multimodal Large Language Model for Scientific Discovery}, year={2025} }