Large language models (LLMs) demonstrate impressive language understanding and contextual learning abilities, making them suitable for natural language processing (NLP) tasks and complex mathematical reasoning. However, when applied to mathematical reasoning tasks, LLMs often struggle to generate correct reasoning steps and answers despite having high probabilities for the solutions. To overcome this limitation and enhance the mathematical reasoning capabilities of fine-tuned LLMs without additional fine-tuning steps, we propose a method that incorporates Monte Carlo Tree Search (MCTS) and a lightweight energy function to rank decision steps and enable immediate reaction and precise reasoning. Specifically, we re-formulate the fine-tuned LLMs into a Residual-based Energy Model (Residual-EBM) and employ noise contrastive estimation to estimate the energy function's parameters. We then utilize MCTS with the energy function as a path verifier to search the output space and evaluate the reasoning path. Through extensive experiments on two mathematical reasoning benchmarks, GSM8k and AQUA-RAT, we demonstrate the exceptional capabilities of our method, which significantly improves the pass@1 metric of the fine-tuned model without requiring additional fine-tuning or reinforcement learning with human feedback alignment.
Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition（脑启发式感知与认知MoE实验室）；University of Science and Technology of China（中国科学技术大学）；Noah's Ark Lab, Huawei Technologies（华为技术诺亚实验室）；Tianjin University（天津大学）
ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile Hardware Development
Computer Department National University of Defense Technology（国防科技大学计算机系）；School of Computer & Communication Engineering University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；Intelligent Microelectronics Center Qiyuan Lab（智能微电子中心启元实验室）；College of Computer Science and Technology National University of Defense Technology（国防科技大学计算机科学与技术学院）；Defense Innovation Institute Academy of Military Science（军事科学创新院）
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；ARC Lab, Tencent PCG（腾讯PCG ARC实验室）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）
Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models
School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Seed-Foundation-Model, ByteDance（字节跳动种子大模型部）；Capital University of Economics and Business（首都经济贸易大学）
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
AnnLab（安澜实验室）；Institute of Semiconductors, Chinese Academy of Sciences（中国科学院半导体研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology（北京市半导体神经网络智能感知与计算重点实验室）；Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences（中国科学院大学材料科学与光电技术学院）
DAG-Math: Graph-of-Thought Guided Mathematical Reasoning in LLMs
Department of Statistics, University of Warwick（沃里克大学统计系）；Google DeepMind（谷歌DeepMind）；Department of Applied Mathematics, Hong Kong Polytechnic University（香港理工大学应用数学系）；School of Mathematical Sciences, Institute of Natural Sciences and MOE-LSC, Shanghai Jiao Tong University（上海交通大学数学科学学院）
Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation
Nanjing University of Information Science \& Technology Nanjing China；East China Normal University Shanghai China；The Hong Kong University of Science；Brown University Providence America；Southwest Jiaotong University Chengdu China；Nanjing University of Information Science \& Technology；East China Normal University；Brown University；Southwest Jiaotong University
提出无需训练的框架Draw with Thought，通过认知思维链推理将科学图表重建为可编辑的mxGraph XML代码，并发布Plot2XML基准，实验证明该方法能生成高保真且结构有效的重建结果。
Proposing and solving olympiad geometry with guided tree search
Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院（BIGAI））；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）
MARGE: Improving Math Reasoning for LLMs with Guided Exploration
Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China（清华大学交叉信息学院）；Alibaba Group（阿里巴巴集团）；Shanghai Qi Zhi Institute, Shanghai, China（上海启智研究院）
Partial Reasoning in Language Models: Search and Refinement Guided by Uncertainty
Advanced Knowledge Center for Immersive Technologies (AKCIT)（沉浸式技术高级知识中心）；Federal University of Goiás, Brazil（巴西戈亚斯联邦大学）；OATML, University of Oxford（牛津大学OATML）
