151 citations · 227 across the 12 of their papers we have counts for
12 papers
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-AI, Anyi Xu, Bangcai Lin +315
We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…
DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
Huajian Xin, Daya Guo, Zhihong Shao +6
Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability. Although large language models (LLMs) show promise in mathem…
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Zhizhou Ren, Anji Liu, Yitao Liang +2
Learning new task-specific skills from a few trials is a fundamental challenge for artificial intelligence. Meta reinforcement learning (meta-RL) tackles this problem by learning t…
Self-Organized Polynomial-Time Coordination Graphs
Qianlan Yang, Weijun Dong, Zhizhou Ren +3
Coordination graph is a promising approach to model agent collaboration in multi-agent reinforcement learning. It conducts a graph-based value factorization and induces explicit co…
Learning Long-Term Reward Redistribution via Randomized Return Decomposition
Zhizhou Ren, Ruihan Guo, Yuan Zhou +1
Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to fut…
On the Estimation Bias in Double Q-Learning
Zhizhou Ren, Guangxiang Zhu, Hao Hu +3
Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation. Its variants in the deep Q-le…
Off-Policy Reinforcement Learning with Delayed Rewards
Beining Han, Zhizhou Ren, Zuofan Wu +2
We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-world tasks, instant rewards are often not readily accessible or even defined immediately af…
Generalizable Episodic Memory for Deep Reinforcement Learning
Hao Hu, Jianing Ye, Guangxiang Zhu +2
Episodic memory-based methods can rapidly latch onto past successful strategies by a non-parametric memory and improve sample efficiency of traditional reinforcement learning. Howe…
QPLEX: Duplex Dueling Multi-Agent Q-Learning
Jianhao Wang, Zhizhou Ren, Terry Liu +2
We explore value-based multi-agent reinforcement learning (MARL) in the popular paradigm of centralized training with decentralized execution (CTDE). CTDE has an important concept,…
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
Jianhao Wang, Zhizhou Ren, Beining Han +2
Value factorization is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings, which balances the learning scalability and the re…
Exploration via Hindsight Goal Generation
Zhizhou Ren, Kefan Dong, Yuan Zhou +2
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a func…
Object-Oriented Dynamics Learning through Multi-Level Abstraction
Guangxiang Zhu, Jianhao Wang, Zhizhou Ren +2
Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structur…