arXiv:2601.03822v2 Announce Type: replace
Abstract: Large language models (LLMs) can achieve strong reasoning performance with sufficient computation, but they do not inherently know how much computa...
By Muyang Zhao, Chunhao Tian, Qi Qi, Hao Sun
arXiv:2607. 06987v1 Announce Type: new Abstract: Reinforcement learning (RL) has become the standard paradigm for enhancing the complex reasoning capabilities of large language models (LLMs).
By Chongyu Fan, Pengfei Liu, Jingjia Huang, Sijia Liu, Yi Lin
arXiv:2603. 25184v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become essential for post-training large language models (LLMs) in reasoning tasks.
By Jiahao Wu, Ning Lu, Shengcai Liu, Kun Wang, Yanting Yang, Bailong Lin, Chen Jason Zhang, Li Qing, Ke Tang
arXiv:2606. 25832v1 Announce Type: new Abstract: Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented large language models (LLMs).
By Ke Zhao, Zixiang Di, Hong Qian, Xiang Shu, Yaolin Wen, Qitao Shi, Bingdong Li, Xingyu Lu, Xiangfeng Wang, Jun Zhou, Ke Tang, Yang Yu
The paper introduces Graph Direct Preference Optimization (GraphDPO), a generalization of Direct Preference Optimization that uses directed acyclic graphs to capture richer preference structures from multiple rollouts per prompt. GraphDPO encodes dominance relations as edges and optimizes a Plackett–Luce-inspired objective that aggregates supervision over dominated neighborhoods, preserving transitivity and handling ties or incomparable responses via an equivalence-class construction. Experiments on reasoning and program synthesis tasks show that this graph-structured approach outperforms traditional pairwise and listwise alignment objectives while maintaining linear per‑prompt complexity.
By Ning Liu, Chuanneng Sun, Kristina Klinkner, Shervin Malmasi
arXiv:2607. 11089v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting.
By Mohammed Ehab, Aymane El Gadarri, Vivek F. Farias, Adam Jozefiak, Ciamac C. Moallemi