2 citations · 3 across the 7 of their papers we have counts for
8 papers
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents
Xingjian Diao, Tianyu Yang, Wenjun Huang +8
Reinforcement learning improves tool-use agents, yet fine-grained credit assignment remains challenging even with step-level feedback. An observed error may be inherited from an ea…
Towards Cost-effective LLMs Routing with Batch Prompting
Haotian Xu, Kangfei Zhao, Jiadong Xie
Large Language Model (LLM) serving systems must balance task performance against monetary cost. Two prominent optimization techniques have emerged independently: LLM routing, which…
Argus: Evidence Assembly for Scalable Deep Research Agents
Zhen Zhang, Liangcai Su, Zhuo Chen +7
Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single trajectory, while recent state of…
Fractional Macroscopic Fluctuation Theory for a Superdiffusive Ginzburg-Landau dynamics
Cedric Bernardin, Patricia Gonçalves, João Pedro Mangi
We investigate a boundary-driven Ginzburg-Landau dynamics with long-range interactions. In the hydrodynamic limit, the macroscopic evolution is governed by a fractional heat equati…
VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning
Zhe Gao, Shiyu Shen, Taifeng Chai +7
Existing Multimodal Large Language Models (MLLMs) often suffer from hallucinations in long video understanding (LVU), primarily due to the imbalance between textual and visual toke…
GEM: A Gym for Agentic LLMs
Zichen Liu, Anya Sims, Keyu Duan +16
The training paradigm for large language models (LLMs) is moving from static datasets to experience-based learning, where agents acquire skills via interacting with complex environ…
REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization
Jian Hu, Jason Klein Liu, Haotian Xu +1
Reinforcement Learning from Human Feedback~(RLHF) plays a crucial role in aligning Large Language Models~(LLMs). The dominant algorithm, Proximal Policy Optimization~(PPO), employs…
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
Jian Hu, Xibin Wu, Wei Shen +12
Large Language Models (LLMs) fine-tuned via Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) significantly improve the al…