1 citations · 3 across the 14 of their papers we have counts for
15 papers
Learning to Plan from Random Exploration
Deqian Kong, Guangyan Sun, Sheng Cheng +6
Random exploration reveals how an environment can be traversed before a goal is specified. Can this experience support long-range planning without policy-improvement training? Our…
Inference-Time Rethinking with Latent Thought Vectors for Math Reasoning
Deqian Kong, Minglu Zhao, Aoyang Qin +10
Standard chain-of-thought reasoning generates a solution in a single forward pass, committing irrevocably to each token and lacking a mechanism to recover from early errors. We int…
Generative Actor Critic
Aoyang Qin, Deqian Kong, Wei Wang +3
Conventional Reinforcement Learning (RL) algorithms, typically focused on estimating or maximizing expected returns, face challenges when refining offline pretrained models with on…
Reasoning Curriculum: Bootstrapping Broad LLM Reasoning from Math
Bo Pang, Deqian Kong, Silvio Savarese +2
Reinforcement learning (RL) can elicit strong reasoning in large language models (LLMs), yet most open efforts focus on math and code. We propose Reasoning Curriculum, a simple two…
FFT-Accelerated Auxiliary Variable MCMC for Fermionic Lattice Models: A Determinant-Free Approach with Complexity
Deqian Kong, Shi Feng, Jianwen Xie +1
We introduce a Markov Chain Monte Carlo (MCMC) algorithm that dramatically accelerates the simulation of quantum many-body systems, a grand challenge in computational science. Stat…
Place Cells as Multi-Scale Position Embeddings: Random Walk Transition Kernels for Path Planning
Minglu Zhao, Dehong Xu, Deqian Kong +2
The hippocampus supports spatial navigation by encoding cognitive maps through collective place cell activity. We model the place cell population as non-negative spatial embeddings…
Latent Adaptive Planner for Dynamic Manipulation
Donghun Noh, Deqian Kong, Minglu Zhao +4
We present the Latent Adaptive Planner (LAP), a trajectory-level latent-variable policy for dynamic nonprehensile manipulation (e.g., box catching) that formulates planning as infe…
Latent Thought Models with Variational Bayes Inference-Time Computation
Deqian Kong, Minglu Zhao, Dehong Xu +8
We propose a novel class of language models, Latent Thought Models (LTMs), which incorporate explicit latent thought vectors that follow an explicit prior model in latent space. Th…
A minimalistic representation model for head direction system
Minglu Zhao, Dehong Xu, Deqian Kong +2
We present a minimalistic representation model for the head direction (HD) system, aiming to learn a high-dimensional representation of head direction that captures essential prope…
DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting
Eric Hanchen Jiang, Zhi Zhang, Dinghuai Zhang +9
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world…
Latent Space Energy-based Neural ODEs
Sheng Cheng, Deqian Kong, Jianwen Xie +3
This paper introduces novel deep dynamical models designed to represent continuous-time sequences. Our approach employs a neural emission model to generate each data point in the t…
"Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood
Peiyu Yu, Dinghuai Zhang, Hengzhi He +10
Noise Contrastive Estimation (NCE) has fueled major breakthroughs in representation learning and generative modeling. Yet a long-standing challenge remains: accurately estimating r…
Molecule Design by Latent Prompt Transformer
Deqian Kong, Yuhao Huang, Jianwen Xie +8
This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constr…
Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference
Deqian Kong, Dehong Xu, Minglu Zhao +6
In tasks aiming for long-term returns, planning becomes essential. We study generative modeling for planning with datasets repurposed from offline reinforcement learning. Specifica…
Molecule Design by Latent Prompt Transformer
Deqian Kong, Yuhao Huang, Jianwen Xie +1
This paper proposes a latent prompt Transformer model for solving challenging optimization problems such as molecule design, where the goal is to find molecules with optimal values…