10 papers
GR-Dexter Technical Report
Ruoshi Wen, Guangzeng Chen, Zhongren Cui +23
Vision-language-action (VLA) models have enabled language-conditioned, long-horizon robot manipulation, but most existing systems are limited to grippers. Scaling VLA policies to b…
Learning Adaptive Parallel Reasoning with Language Models
Jiayi Pan, Xiuyu Li, Long Lian +6
Scaling inference-time computation has substantially improved the reasoning capabilities of language models. However, existing methods have significant limitations: serialized chai…
Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
Junhong Shen, Hao Bai, Lunjun Zhang +8
The current paradigm of test-time scaling relies on generating long reasoning traces ("thinking" more) before producing a response. In agent problems that require interaction, this…
Self-Challenging Language Model Agents
Yifei Zhou, Sergey Levine, Jason Weston +2
Large language models are quickly becoming the foundation for intelligent agents that are capable of using tools. However, training such agents is challenging because it requires h…
SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks
Yifei Zhou, Song Jiang, Yuandong Tian +4
Large language model (LLM) agents need to perform multi-turn interactions in real-world tasks. However, existing multi-turn RL algorithms for optimizing LLM agents fail to perform…
Digi-Q: Learning Q-Value Functions for Training Device-Control Agents
Hao Bai, Yifei Zhou, Li Erran Li +2
While a number of existing approaches for building foundation model agents rely on prompting or fine-tuning with human demonstrations, it is not sufficient in dynamic environments…