4 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
Yunseon Choi, Sangmin Bae, Seonghyun Ban +6
With the advent of foundation models, prompt tuning has positioned itself as an important technique for directing model behaviors and eliciting desired responses. Prompt tuning reg…
Video In-context Learning: Autoregressive Transformers are Zero-Shot Video Imitators
Wentao Zhang, Junliang Guo, Tianyu He +3
People interact with the real-world largely dependent on visual signal, which are ubiquitous and illustrate detailed demonstrations. In this paper, we explore utilizing visual sign…
Empowering Large Language Models on Robotic Manipulation with Affordance Prompting
Guangran Cheng, Chuheng Zhang, Wenzhe Cai +3
While large language models (LLMs) are successful in completing various language processing tasks, they easily fail to interact with the physical world by generating control sequen…
DPO Meets PPO: Reinforced Token Optimization for RLHF
Han Zhong, Zikang Shan, Guhao Feng +6
In the classical Reinforcement Learning from Human Feedback (RLHF) framework, Proximal Policy Optimization (PPO) is employed to learn from sparse, sentence-level rewards -- a chall…