# Valley3: Scaling Omni Foundation Models for E-commerce arXiv: https://arxiv.org/abs/2605.01278 ยท HTML: https://mlsanity.com/paper/2605.01278/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. - Builds on 39 of its 49 references that are in the map. - First posted on arXiv 2 May 2026. ## Placed in - LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/) - Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/) ## Builds on (most cited first, 30 of 39) - [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025) - [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023) - [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022) - [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023) - [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://mlsanity.com/paper/2409.02813/) (2024) - [Qwen3-Omni Technical Report](https://mlsanity.com/paper/2509.17765/) (2025) - [VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models](https://mlsanity.com/paper/2407.11691/) (2024) - [MLVU: Benchmarking Multi-task Long Video Understanding](https://mlsanity.com/paper/2406.04264/) (2024) - [MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2410.19168/) (2024) - [Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos](https://mlsanity.com/paper/2501.13826/) (2025) - [Valley: Video Assistant with Large Language model Enhanced abilitY](https://mlsanity.com/paper/2306.07207/) (2023) - [VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction](https://mlsanity.com/paper/2501.01957/) (2025) - [MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe](https://mlsanity.com/paper/2509.18154/) (2025) - [VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset](https://mlsanity.com/paper/2305.18500/) (2023) - [Baichuan-Omni-1.5 Technical Report](https://mlsanity.com/paper/2501.15368/) (2025) - [MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines](https://mlsanity.com/paper/2409.12959/) (2024) - [Ola: Pushing the Frontiers of Omni-Modal Language Model](https://mlsanity.com/paper/2502.04328/) (2025) - [PolyMath: Evaluating Mathematical Reasoning in Multilingual Contexts](https://mlsanity.com/paper/2504.18428/) (2025) - [LongVALE: Vision-Audio-Language-Event Benchmark Towards Time-Aware Omni-Modal Perception of Long Videos](https://mlsanity.com/paper/2411.19772/) (2024) - [Seed1.8 Model Card: Towards Generalized Real-World Agency](https://mlsanity.com/paper/2603.20633/) (2026) - [EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce](https://mlsanity.com/paper/2308.06966/) (2023) - [eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data](https://mlsanity.com/paper/2402.08831/) (2024) - [Jina CLIP: Your CLIP Model Is Also Your Text Retriever](https://mlsanity.com/paper/2405.20204/) (2024) - [Omni-Video: Democratizing Unified Video Understanding and Generation](https://mlsanity.com/paper/2507.06119/) (2025) - [OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe](https://mlsanity.com/paper/2511.16334/) (2025) - [ShoppingBench: A Real-World Intent-Grounded Shopping Benchmark for LLM-based Agents](https://mlsanity.com/paper/2508.04266/) (2025) - [Valley2: Exploring Multimodal Models with Scalable Vision-Language Design](https://mlsanity.com/paper/2501.05901/) (2025) - [MM-DeepResearch: A Simple and Effective Multimodal Agentic Search Baseline](https://mlsanity.com/paper/2603.01050/) (2026) - [OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesis](https://mlsanity.com/paper/2501.04561/) (2025) - [TaoSR1: The Thinking Model for E-commerce Relevance Search](https://mlsanity.com/paper/2508.12365/) (2025) ## Built on by (most cited first, 1 of 1) - [TLive-Omni: An Omni-Modal Understanding Model for E-Commerce Live Streaming](https://mlsanity.com/paper/2608.20958/) (2026) ## Abstract (arXiv) In this work, we present Valley3, an omni multimodal large language model (MLLM) developed for diverse global e-commerce tasks, with unified understanding and reasoning capabilities across text, images, video, and audio. A key feature of Valley3 is its native multilingual audio capability for e-commerce, developed by extending vision-language models to better support crucial audio-visual tasks, particularly in short-video scenarios. To achieve this, we carefully design a four-stage omni e-commerce continued pre-training pipeline, through which Valley3 progressively acquires audio understanding, cross-modal instruction-following, e-commerce domain knowledge, and long-context reasoning capabilities, ultimately evolving into an omni model for diverse e-commerce scenarios. Then, we further improve Valley3 through post-training to encourage long-chain reasoning with controllable reasoning modes, enabling one non-thinking mode and three distinct levels of thinking, thereby balancing inference efficiency in simple scenarios with deep reasoning for complex applications. Moreover, we equip Valley3 with agentic search capabilities to proactively invoke search tools and acquire task-relevant information for e-commerce deep research tasks. To comprehensively assess the capabilities of Valley3, we construct an omni e-commerce benchmark spanning 6 tasks. Experimental results show that Valley3 consistently outperforms strong baselines on our in-house and open-source e-commerce benchmarks, while remaining competitive on general-domain benchmarks. Our code and model weights are available at https://github.com/bytedance/Valley.