5 papers
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…
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…
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…
o1-Coder: an o1 Replication for Coding
Yuxiang Zhang, Shangxi Wu, Yuqi Yang +4
The technical report introduces O1-CODER, an attempt to replicate OpenAI's o1 model with a focus on coding tasks. It integrates reinforcement learning (RL) and Monte Carlo Tree Sea…