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20162025
most citedZ-Forcing: Training Stochastic Recurrent Networks

32 citations · 53 across the 12 of their papers we have counts for

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8 papers · 1 filter

cs.LG2024

Sub-goal Distillation: A Method to Improve Small Language Agents

Maryam Hashemzadeh, Elias Stengel-Eskin, Sarath Chandar +1

While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational requirements and restricted number of calls…

cs.LG2024

Language-guided Skill Learning with Temporal Variational Inference

Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin +4

We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajecto…

cs.LG20241 cited

Policy Improvement using Language Feedback Models

Victor Zhong, Dipendra Misra, Xingdi Yuan +1

We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instructio…

cs.LG2020

Graph Policy Network for Transferable Active Learning on Graphs

Shengding Hu, Zheng Xiong, Meng Qu +4

Graph neural networks (GNNs) have been attracting increasing popularity due to their simplicity and effectiveness in a variety of fields. However, a large number of labeled data is…

cs.LG2019

Unsupervised State Representation Learning in Atari

Ankesh Anand, Evan Racah, Sherjil Ozair +3

State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent agents that can perform a wide variety of…

cs.LG2018

Towards Solving Text-based Games by Producing Adaptive Action Spaces

Ruo Yu Tao, Marc-Alexandre Côté, Xingdi Yuan +1

To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success. Recent attempts at solving text-based games…