1 citations · 1 across the 11 of their papers we have counts for
11 papers
Adaptive Rollout Allocation for Online Reinforcement Learning with Verifiable Rewards
Hieu Trung Nguyen, Bao Nguyen, Wenao Ma +3
Sampling efficiency is a key bottleneck in reinforcement learning with verifiable rewards. Existing group-based policy optimization methods, such as GRPO, allocate a fixed number o…
Reasoning Planning for Language Models
Bao Nguyen, Hieu Trung Nguyen, Ruifeng She +2
Selecting an appropriate reasoning method for a given query remains a key challenge in language model generation. Existing approaches typically generate multiple candidate response…
Structured Pruning for Diverse Best-of-N Reasoning Optimization
Hieu Trung Nguyen, Bao Nguyen, Viet Anh Nguyen
Model pruning in transformer-based language models, traditionally viewed as a means of achieving computational savings, can enhance the model's reasoning capabilities. In this work…
Task-driven Layerwise Additive Activation Intervention
Hieu Trung Nguyen, Bao Nguyen, Binh Nguyen +1
Modern language models (LMs) have significantly advanced generative modeling in natural language processing (NLP). Despite their success, LMs often struggle with adaptation to new…
Probe-Free Low-Rank Activation Intervention
Chonghe Jiang, Bao Nguyen, Anthony Man-Cho So +1
Language models (LMs) can produce texts that appear accurate and coherent but contain untruthful or toxic content. Inference-time interventions that edit the hidden activations hav…
Distributional Surgery for Language Model Activations
Bao Nguyen, Binh Nguyen, Duy Nguyen +1
Language models, while capable of generating remarkably coherent and seemingly accurate text, can occasionally produce undesirable content, including harmful or toxic outputs. In t…
Generative Conditional Distributions by Neural (Entropic) Optimal Transport
Bao Nguyen, Binh Nguyen, Hieu Trung Nguyen +1
Learning conditional distributions is challenging because the desired outcome is not a single distribution but multiple distributions that correspond to multiple instances of the c…
Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation
Duy Nguyen, Bao Nguyen, Viet Anh Nguyen
Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. Most existing methods in the literature gen…
Bellman Optimal Stepsize Straightening of Flow-Matching Models
Bao Nguyen, Binh Nguyen, Viet Anh Nguyen
Flow matching is a powerful framework for generating high-quality samples in various applications, especially image synthesis. However, the intensive computational demands of these…
Efficient Failure Pattern Identification of Predictive Algorithms
Bao Nguyen, Viet Anh Nguyen
Given a (machine learning) classifier and a collection of unlabeled data, how can we efficiently identify misclassification patterns presented in this dataset? To address this prob…
The complexity of unsupervised learning of lexicographic preferences
Hélène Fargier, Pierre-François Gimenez, Jérôme Mengin +1
This paper considers the task of learning users' preferences on a combinatorial set of alternatives, as generally used by online configurators, for example. In many settings, only…