4 citations · 6 across the 10 of their papers we have counts for
7 papers · 1 filter
Advantage Shaping as Surrogate Reward Maximization: Unifying Pass@K Policy Gradients
Christos Thrampoulidis, Sadegh Mahdavi, Wenlong Deng
This note reconciles two seemingly distinct approaches to policy gradient optimization for the Pass@K objective in reinforcement learning with verifiable rewards: (1) direct REINFO…
Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning
Wenlong Deng, Yi Ren, Yushu Li +4
Reinforcement learning with verifiable rewards has significantly advanced the reasoning capabilities of large language models, yet how to explicitly steer training toward explorati…
On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization
Wenlong Deng, Yi Ren, Muchen Li +3
Reinforcement learning (RL) has become popular in enhancing the reasoning capabilities of large language models (LLMs), with Group Relative Policy Optimization (GRPO) emerging as a…
DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models
Wenlong Deng, Yize Zhao, Vala Vakilian +3
Storing open-source fine-tuned models separately introduces redundancy and increases response times in applications utilizing multiple models. Delta-parameter pruning (DPP), partic…
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning
Wenlong Deng, Christos Thrampoulidis, Xiaoxiao Li
Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art performance with improved efficiency in various computer vision tasks. This suggests a promising p…
Memorization Capacity of Multi-Head Attention in Transformers
Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis
Transformers have become the go-to architecture for language and vision tasks, yet their theoretical properties, especially memorization capacity, remain elusive. This paper invest…
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf +3
In this paper, we study the OOD generalization of neural algorithmic reasoning tasks, where the goal is to learn an algorithm (e.g., sorting, breadth-first search, and depth-first…