2 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models
Xiaohang Tang, Keyue Jiang, Che Liu +4
Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intractability of the policy likeli…
Robust Adversarial Reinforcement Learning in Stochastic Games via Sequence Modeling
Xiaohang Tang, Zhuowen Cheng, Satyabrat Kumar
The Transformer, a highly expressive architecture for sequence modeling, has recently been adapted to solve sequential decision-making, most notably through the Decision Transforme…
wd1: Weighted Policy Optimization for Reasoning in Diffusion Language Models
Xiaohang Tang, Rares Dolga, Sangwoong Yoon +1
Improving the reasoning capabilities of diffusion-based large language models (dLLMs) through reinforcement learning (RL) remains an open problem. The intractability of dLLMs likel…
Robust Multi-Objective Controlled Decoding of Large Language Models
Seongho Son, William Bankes, Sangwoong Yoon +3
We introduce Robust Multi-Objective Decoding (RMOD), a novel inference-time algorithm that robustly aligns Large Language Models (LLMs) to multiple human objectives (e.g., instruct…
RSPO: Regularized Self-Play Alignment of Large Language Models
Xiaohang Tang, Sangwoong Yoon, Seongho Son +3
Self-play-based policy optimization has emerged as an effective approach for fine-tuning large language models (LLMs), formulating preference optimization as a two-player game. How…
Adversarially Robust Decision Transformer
Xiaohang Tang, Afonso Marques, Parameswaran Kamalaruban +1
Decision Transformer (DT), as one of the representative Reinforcement Learning via Supervised Learning (RvS) methods, has achieved strong performance in offline learning tasks by l…