1 citations · 1 across the 5 of their papers we have counts for
5 papers
Meta-RL Induces Exploration in Language Agents
Yulun Jiang, Liangze Jiang, Damien Teney +2
Reinforcement learning (RL) has enabled the training of large language model (LLM) agents to interact with the environment and to solve multi-turn long-horizon tasks. However, the…
MARBLE: A Hard Benchmark for Multimodal Spatial Reasoning and Planning
Yulun Jiang, Yekun Chai, Maria Brbić +1
The ability to process information from multiple modalities and to reason through it step-by-step remains a critical challenge in advancing artificial intelligence. However, existi…
Large (Vision) Language Models are Unsupervised In-Context Learners
Artyom Gadetsky, Andrei Atanov, Yulun Jiang +4
Recent advances in large language and vision-language models have enabled zero-shot inference, allowing models to solve new tasks without task-specific training. Various adaptation…
Let Go of Your Labels with Unsupervised Transfer
Artyom Gadetsky, Yulun Jiang, Maria Brbic
Foundation vision-language models have enabled remarkable zero-shot transferability of the pre-trained representations to a wide range of downstream tasks. However, to solve a new…
Learning Cluster Structured Sparsity by Reweighting
Yulun Jiang, Lei Yu, Haijian Zhang +1
Recently, the paradigm of unfolding iterative algorithms into finite-length feed-forward neural networks has achieved a great success in the area of sparse recovery. Benefit from a…