106 citations · 996 across the 72 of their papers we have counts for
10 papers · 1 filter
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Haipeng Luo, Qingfeng Sun, Can Xu +8
Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, mo…
Regret Matching+: (In)Stability and Fast Convergence in Games
Gabriele Farina, Julien Grand-Clément, Christian Kroer +2
Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery…
No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions
Tiancheng Jin, Junyan Liu, Chloé Rouyer +3
Existing online learning algorithms for adversarial Markov Decision Processes achieve regret after rounds of interactions even if the loss functions are chosen…
Uncoupled and Convergent Learning in Two-Player Zero-Sum Markov Games with Bandit Feedback
Yang Cai, Haipeng Luo, Chen-Yu Wei +1
We revisit the problem of learning in two-player zero-sum Markov games, focusing on developing an algorithm that is uncoupled, convergent, and rational, with non-asymptotic converg…
Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms
Tiancheng Jin, Junyan Liu, Haipeng Luo
We study the problem of designing adaptive multi-armed bandit algorithms that perform optimally in both the stochastic setting and the adversarial setting simultaneously (often kno…
Practical Contextual Bandits with Feedback Graphs
Mengxiao Zhang, Yuheng Zhang, Olga Vrousgou +2
While contextual bandit has a mature theory, effectively leveraging different feedback patterns to enhance the pace of learning remains unclear. Bandits with feedback graphs, which…
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints
Akhil Agnihotri, Rahul Jain, Haipeng Luo
Reinforcement Learning (RL) for constrained MDPs (CMDPs) is an increasingly important problem for various applications. Often, the average criterion is more suitable than the disco…
Refined Regret for Adversarial MDPs with Linear Function Approximation
Yan Dai, Haipeng Luo, Chen-Yu Wei +1
We consider learning in an adversarial Markov Decision Process (MDP) where the loss functions can change arbitrarily over episodes and the state space can be arbitrarily large.…
Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?
Wenhao Wu, Haipeng Luo, Bo Fang +2
Most existing text-video retrieval methods focus on cross-modal matching between the visual content of videos and textual query sentences. However, in real-world scenarios, online…
Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models
Wenhao Wu, Xiaohan Wang, Haipeng Luo +3
Vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such pow…