Multimodal reasoning in vision-language models (VLMs) typically relies on a two-stage process: supervised fine-tuning (SFT) and reinforcement learning (RL). In standard SFT, all tokens contribute equally to the loss, even though reasoning data are inherently token-imbalanced. Long <think> traces overshadow short but task-critical <answer> segments, leading to verbose reasoning and inaccurate answers. We propose SCALe (Scheduled Curriculum Adaptive Loss), which explicitly separates supervision over reasoning and answer segments using dynamic, length-independent weighting. Unlike vanilla SFT, which overweights the <think> segment, SCALe-SFT gradually shifts the focus from <think> to <answer> throughout training via a cosine scheduling policy, encouraging concise and well-grounded reasoning. We evaluate SCALe across diverse benchmarks and architectures. Results show that SCALe consistently improves accuracy over vanilla SFT and matches the performance of the full two-phase SFT + GRPO pipeline while requiring only about one-seventh of the training time, making it a lightweight yet effective alternative. When combined with GRPO, SCALe achieves the best overall performance, highlighting its value both as a standalone method and as a strong foundation for reinforcement refinement.
Don't Wait to Reply: Towards Responsive yet Thoughtful Dialogue through Proactive Thinking
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University（清华大学计算机科学与技术系、人工智能研究院）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；Tencent（腾讯）
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Zhongguancun Academy（中关村学院）；Shenzhen Loop Area Institute（深圳河套学院）
Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
University of Science and Technology of China（中国科学技术大学）；cmss.chinamobile.com（中移（苏州）软件技术有限公司）；Lin Gang Laboratory（林冈实验室）；ShanghaiTech University（上海科技大学）
Unlocking Recursive Thinking of LLMs: Alignment via Refinement
Soochow University（苏州大学）；Zhipu AI（智谱AI）；Tsinghua University（清华大学）；Key Laboratory of Data Intelligence and Advanced Computing, Soochow University（苏州大学数据智能与先进计算重点实验室）
Tailored Teaching with Balanced Difficulty: Elevating Reasoning in Multimodal Chain-of-Thought via Prompt Curriculum
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Hunan Vanguard Group Corporation Co., Ltd.（湖南 Vanguard集团有限公司）；Huaneng Information Technology Co., Ltd.（华能信息技术有限公司）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；PowerChina Huadong Engineering Co., Ltd.（中国电建华东工程有限公司）
The Evolution of Thought: Tracking LLM Overthinking via Reasoning Dynamics Analysis
State Key Laboratory of AI Safety,Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
Computer Science and Engineering, University of Michigan, Ann Arbor, MI（计算机科学与工程系，密歇根大学，安娜堡分校）；Department of Political Science, Northeastern University, Boston, MA（政治学系，东北大学，波士顿分校）
该研究提出融合认知双记忆与个性化慢思考的 PRIME 框架，并构建基于 Reddit CMV 的长上下文个性化数据集，实验证明其可捕捉动态用户偏好。
This paper presents a theoretical, idealized model of the thinking process with the following characteristics: 1) the model can produce complex thought sequences and can be generalized to new inputs, 2) it can receive and maintain input information indefinitely for the generation of thoughts and later use, and 3) it supports learning while executing. The crux of the model lies within the concept of internal consistency, or the generated thoughts should always be consistent with the inputs from which they are created. Its merit, apart from the capability to generate new creative thoughts from a…
