4 papers
ACCORD: Action-Conditioned Contextual Grounding for Language Agents
Lai Jiang, Cheng Qian, Zhenhailong Wang +3
User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment. For large language model (LLM) agents operating in informa…
You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences
Ninad Daithankar, Alexi Gladstone, Yann LeCun +1
Progress in AI has largely been driven by methods that assume less. As compute and data increase, approaches with weaker inductive biases generally outperform those with stronger a…
A Survey on Post-training of Large Language Models
Guiyao Tie, Zeli Zhao, Dingjie Song +23
The emergence of Large Language Models (LLMs) has fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational system…
DyMU: Dynamic Merging and Virtual Unmerging for Efficient VLMs
Zhenhailong Wang, Senthil Purushwalkam, Caiming Xiong +3
We present DyMU, an efficient, training-free framework that dynamically reduces the computational burden of vision-language models (VLMs) while maintaining high task performance. O…