8 papers
MTA: Multi-Granular Trajectory Alignment for Large Language Model Distillation
Pham Khanh Chi, Quoc Phong Dao, Thuat Nguyen +3
Knowledge distillation is a key technique for compressing large language models (LLMs), but most existing methods align representations at fixed layers or token-level outputs, igno…
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining
Phung Gia Huy, Hai An Vu, Minh-Phuc Truong +4
Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Representation Learning (MRL) of…
CTPD: Cross Tokenizer Preference Distillation
Truong Nguyen, Phi Van Dat, Ngan Nguyen +3
While knowledge distillation has seen widespread use in pre-training and instruction tuning, its application to aligning language models with human preferences remains underexplore…
Preference-Guided Learning for Sparse-Reward Multi-Agent Reinforcement Learning
The Viet Bui, Tien Mai, Hong Thanh Nguyen
We study the problem of online multi-agent reinforcement learning (MARL) in environments with sparse rewards, where reward feedback is not provided at each interaction but only rev…
MisoDICE: Multi-Agent Imitation from Unlabeled Mixed-Quality Demonstrations
The Viet Bui, Tien Mai, Hong Thanh Nguyen
We study offline imitation learning (IL) in cooperative multi-agent settings, where demonstrations have unlabeled mixed quality - containing both expert and suboptimal trajectories…
O-MAPL: Offline Multi-agent Preference Learning
The Viet Bui, Tien Mai, Hong Thanh Nguyen
Inferring reward functions from demonstrations is a key challenge in reinforcement learning (RL), particularly in multi-agent RL (MARL), where large joint state-action spaces and c…
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization
The Viet Bui, Thanh Hong Nguyen, Tien Mai
Offline reinforcement learning (RL) has garnered significant attention for its ability to learn effective policies from pre-collected datasets without the need for further environm…
Inverse Factorized Q-Learning for Cooperative Multi-agent Imitation Learning
The Viet Bui, Tien Mai, Thanh Hong Nguyen
This paper concerns imitation learning (IL) (i.e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems. The learning problem un…