177 citations · 191 across the 18 of their papers we have counts for
18 papers
NoC-Out: A Formally-verified Network-on-Chip Library for Rule-based Hardware Designs
Max Kurze, František Farka, Sebastian Ertel
The Network-on-Chip (NoC) is the communication backbone of any multiprocessor chip. A failure of the NoC has severe consequences for the whole system. Yet, no approach exists that…
The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning
Jing Liang, Hongyao Tang, Yi Ma +9
Reinforcement learning (RL) has gained growing attention in large language model (LLM) post-training, yet RL training remains fragile and can suffer from instability or collapse. O…
Key Decision-Makers in Multi-Agent Debates: Who Holds the Power?
Qian Zhang, Yan Zheng, Jinyi Liu +2
Recent studies on LLM agent scaling have highlighted the potential of Multi-Agent Debate (MAD) to enhance reasoning abilities. However, the critical aspect of role allocation strat…
Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model
Jing Liang, Hongyao Tang, Yi Ma +5
Reinforcement Learning (RL) has demonstrated its potential to improve the reasoning ability of Large Language Models (LLMs). One major limitation of most existing Reinforcement Fin…
From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation
Yifu Yuan, Haiqin Cui, Yibin Chen +7
Achieving generalization in robotic manipulation remains a critical challenge, particularly for unseen scenarios and novel tasks. Current Vision-Language-Action (VLA) models, while…
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering
Rong Cheng, Jinyi Liu, Yan Zheng +6
Multi-Hop Question Answering (MHQA) tasks permeate real-world applications, posing challenges in orchestrating multi-step reasoning across diverse knowledge domains. While existing…
From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models
Jinyi Liu, Yan Zheng, Rong Cheng +8
Recent advances in large language models (LLMs) have shown remarkable progress, yet their capacity for logical ``slow-thinking'' reasoning persists as a critical research frontier.…
CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis
Yihang Xiao, Jinyi Liu, Yan Zheng +9
Single-cell RNA sequencing (scRNA-seq) data analysis is crucial for biological research, as it enables the precise characterization of cellular heterogeneity. However, manual manip…
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement
Yiwen Zhu, Jinyi Liu, Wenya Wei +7
Reinforcement Learning (RL) is a widely employed technique in decision-making problems, encompassing two fundamental operations -- policy evaluation and policy improvement. Enhanci…
Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback
Yifu Yuan, Jianye Hao, Yi Ma +6
Reinforcement Learning with Human Feedback (RLHF) has received significant attention for performing tasks without the need for costly manual reward design by aligning human prefere…
SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models
Yibin Chen, Yifu Yuan, Zeyu Zhang +6
Spreadsheets are ubiquitous across the World Wide Web, playing a critical role in enhancing work efficiency across various domains. Large language model (LLM) has been recently att…
Enhancing Robotic Manipulation with AI Feedback from Multimodal Large Language Models
Jinyi Liu, Yifu Yuan, Jianye Hao +4
Recently, there has been considerable attention towards leveraging large language models (LLMs) to enhance decision-making processes. However, aligning the natural language text in…
OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments
Jinyi Liu, Zhi Wang, Yan Zheng +6
In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream principle for directing exploration towards less explored areas, characterized by higher un…
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach
Shixi Lian, Yi Ma, Jinyi Liu +2
Offline reinforcement learning (ORL) has gained attention as a means of training reinforcement learning models using pre-collected static data. To address the issue of limited data…
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning
Jinyi Liu, Yi Ma, Jianye Hao +4
In recent years, data-driven reinforcement learning (RL), also known as offline RL, have gained significant attention. However, the role of data sampling techniques in offline RL h…
ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
Kai Zhao, Jianye Hao, Yi Ma +3
Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the pe…
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Yifu Yuan, Jianye Hao, Fei Ni +6
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitat…
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Jianye Hao, Tianpei Yang, Hongyao Tang +5
Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved significant successes across a wide range of domains, including game AI, autonomo…