1 citations · 1 across the 4 of their papers we have counts for
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Evaluate-as-Action: Self-Evaluated Process Rewards for Retrieval-Augmented Agents
Jiangming Shu, Yuxiang Zhang, Ye Ma +2
Retrieval-augmented agents can query external evidence, yet their reliability in multi-step reasoning remains limited: noisy retrieval may derail multi-hop question answering, whil…
Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks
Yuxiang Zhang, Jiangming Shu, Ye Ma +3
Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existin…
Agent models: Internalizing Chain-of-Action Generation into Reasoning models
Yuxiang Zhang, Yuqi Yang, Jiangming Shu +2
Traditional agentic workflows rely on external prompts to manage interactions with tools and the environment, which limits the autonomy of reasoning models. We position \emph{Large…
OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Yuxiang Zhang, Yuqi Yang, Jiangming Shu +3
OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the potential of reasoning foundation model and offers a new paradigm for fine-tuning beyond simple patter…