6 papers
Low-Bit N-Gram Embeddings for Language Models
Stephen Chung, Wenyu Du
Many recent open-weight language models use n-gram embedding tables to improve performance. These tables store learned representations of short token sequences, such as pairs or tr…
Autonomous Mathematical Discovery in an Open-World Multi-Agent Environment
Stephen Chung, Wenyu Du, William J. Wesley
We study autonomous mathematical discovery in the Station, an open-world multi-agent environment in which AI agents from different model families pursue a shared research goal with…
The Station: An Open-World Environment for AI-Driven Discovery
Stephen Chung, Wenyu Du
We introduce the STATION, an open-world multi-agent environment for autonomous scientific discovery. The Station simulates a complete scientific ecosystem, where agents can engage…
Learning from Peers in Reasoning Models
Tongxu Luo, Wenyu Du, Jiaxi Bi +5
Large Reasoning Models (LRMs) have the ability to self-correct even when they make mistakes in their reasoning paths. However, our study reveals that when the reasoning process sta…
Thinker: Learning to Think Fast and Slow
Stephen Chung, Wenyu Du, Jie Fu
Recent studies show that the reasoning capabilities of Large Language Models (LLMs) can be improved by applying Reinforcement Learning (RL) to question-answering (QA) tasks in area…
Learning from Failures in Multi-Attempt Reinforcement Learning
Stephen Chung, Wenyu Du, Jie Fu
Recent advancements in reinforcement learning (RL) for large language models (LLMs), exemplified by DeepSeek R1, have shown that even a simple question-answering task can substanti…