4 papers · 1 filter
GUARD: Natural Forgetting in Large Reasoning Models via Guided Answer-Reasoning Distillation
Zeyu Yan, Guanghao Zhou, Minghui Qiu +2
Recent advances in large reasoning models (LRMs) have made machine unlearning more challenging, as protected facts or unsafe rationales may surface in intermediate chain-of-thought…
Valley3: Scaling Omni Foundation Models for E-commerce
Zeyu Chen, Guanghao Zhou, Qixiang Yin +6
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 capabilitie…
Your Models Have Thought Enough: Training Large Reasoning Models to Stop Overthinking
Jinyi Han, Ying Huang, Ying Liao +11
Large Reasoning Models (LRMs) have achieved impressive performance on challenging tasks, yet their deep reasoning often incurs substantial computational costs. To achieve efficient…
Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models
Guanghao Zhou, Panjia Qiu, Cen Chen +4
The application of reinforcement learning (RL) to enhance the reasoning capabilities of Multimodal Large Language Models (MLLMs) constitutes a rapidly advancing research area. Whil…