6 papers
U-Fold: Dynamic Intent-Aware Context Folding for User-Centric Agents
Jin Su, Runnan Fang, Yeqiu Li +5
Large language model (LLM)-based agents have been successfully deployed in many tool-augmented settings, but their scalability is fundamentally constrained by context length. Exist…
AutoForge: Automated Environment Synthesis for Agentic Reinforcement Learning
Shihao Cai, Runnan Fang, Jialong Wu +10
Conducting reinforcement learning (RL) in simulated environments offers a cost-effective and highly scalable way to enhance language-based agents. However, previous work has been l…
MGFRec: Towards Reinforced Reasoning Recommendation with Multiple Groundings and Feedback
Shihao Cai, Chongming Gao, Haoyan Liu +4
The powerful reasoning and generative capabilities of large language models (LLMs) have inspired researchers to apply them to reasoning-based recommendation tasks, which require in…
Towards General Agentic Intelligence via Environment Scaling
Runnan Fang, Shihao Cai, Baixuan Li +13
Advanced agentic intelligence is a prerequisite for deploying Large Language Models in practical, real-world applications. Diverse real-world APIs demand precise, robust function-c…
Scaling Agents via Continual Pre-training
Liangcai Su, Zhen Zhang, Guangyu Li +19
Large language models (LLMs) have evolved into agentic systems capable of autonomous tool use and multi-step reasoning for complex problem-solving. However, post-training approache…
K-order Ranking Preference Optimization for Large Language Models
Shihao Cai, Chongming Gao, Yang Zhang +5
To adapt large language models (LLMs) to ranking tasks, existing list-wise methods, represented by list-wise Direct Preference Optimization (DPO), focus on optimizing partial-order…