Search Results for author:
Found 8 papers, 5 papers with code
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity.
OpenAgents: An Open Platform for Language Agents in the Wild
Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).
Lemur: Harmonizing Natural Language and Code for Language Agents
We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents.
Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning
Unlike inverse RL and recent work that uses LLMs to write sparse reward codes, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback.
$\mathcal{Y}$-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning
With the success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions of parameters.
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
In this paper, we take the advantage of previous pre-trained models (PTMs) and propose a novel Chinese Pre-trained Unbalanced Transformer (CPT).
Learning to Teach with Student Feedback
This one-way interaction leads to the teacher's inability to perceive the characteristics of the student and its training progress.
LabelEnc: A New Intermediate Supervision Method for Object Detection
In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems.
