7 citations · 7 across the 9 of their papers we have counts for
10 papers
DREAM: Dynamic Resolution Assignment For Multimodal Multi-agent Debate
Khanh-Binh Nguyen, Van Dai Do, Tien Anh Nguyen +2
Multi-agent debate (MAD) has emerged as an effective paradigm to improve the reasoning capabilities of large language models (LLMs) and is increasingly being extended to multimodal…
Creative Generation via Multi-Agent Debate: Does Debate Suppress Diversity?
Tien Anh Nguyen, Khanh-Binh Nguyen, Van Dai Do +2
Creative generation tasks, such as narrative writing and scientific ideation, demand both high-quality outputs and distinct responses across independent runs to maximize exploratio…
Continual Fine-Tuning of Large Language Models via Program Memory
Hung Le, Svetha Venkatesh
Parameter-Efficient Fine-Tuning (PEFT), particularly Low-Rank Adaptation (LoRA), has become a standard approach for adapting Large Language Models (LLMs) under limited compute. How…
Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention
Manh Nguyen, Anh Nguyen, Dung Nguyen +2
Multi-Agent Debate has emerged as a promising framework for improving the reasoning quality of large language models through iterative inter-agent communication. However, broadcast…
SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning
Dai Do, Manh Nguyen, Svetha Venkatesh +1
Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL). However, such methods require extensive data and compute, ma…
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models
Hung Le, Dai Do, Dung Nguyen +1
Recent advances in fine-tuning large language models (LLMs) with reinforcement learning (RL) have shown promising improvements in complex reasoning tasks, particularly when paired…
Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning
Hung Le, Kien Do, Dung Nguyen +2
Effective decision-making in partially observable environments demands robust memory management. Despite their success in supervised learning, current deep-learning memory models s…
Large Language Models Prompting With Episodic Memory
Dai Do, Quan Tran, Svetha Venkatesh +1
Prompt optimization is essential for enhancing the performance of Large Language Models (LLMs) in a range of Natural Language Processing (NLP) tasks, particularly in scenarios of f…
Variable-Agnostic Causal Exploration for Reinforcement Learning
Minh Hoang Nguyen, Hung Le, Svetha Venkatesh
Modern reinforcement learning (RL) struggles to capture real-world cause-and-effect dynamics, leading to inefficient exploration due to extensive trial-and-error actions. While rec…
Dual Memory Neural Computer for Asynchronous Two-view Sequential Learning
Hung Le, Truyen Tran, Svetha Venkatesh
One of the core tasks in multi-view learning is to capture relations among views. For sequential data, the relations not only span across views, but also extend throughout the view…