Papers5

#LLM Agents

Agentic Reasoning for Large Language Models

Tianxin Wei, Ting-Wei Li et al.Jan 18arXiv

This paper explains how to turn large language models (LLMs) from quiet students that only answer questions into active agents that can plan, act, and learn over time.

#Agentic Reasoning#LLM Agents#In-Context Learning

ET-Agent: Incentivizing Effective Tool-Integrated Reasoning Agent via Behavior Calibration

Intermediate

Yifei Chen, Guanting Dong et al.Jan 11arXiv

ET-Agent is a training framework that teaches AI agents to use tools (like search and code) more wisely, not just to get the right answer.

#Tool-Integrated Reasoning#Behavior Calibration#Self-evolving Data Flywheel

Can We Predict Before Executing Machine Learning Agents?

Intermediate

Jingsheng Zheng, Jintian Zhang et al.Jan 9arXiv

Machine learning agents usually improve by writing code, running it for hours, and then using the results to tweak the next try, which is very slow.

#World Models#Predict-then-Verify#Data-centric AI

AT$^2$PO: Agentic Turn-based Policy Optimization via Tree Search

Intermediate

Zefang Zong, Dingwei Chen et al.Jan 8arXiv

AT2PO is a new way to train AI agents that work in several turns, like asking the web a question, reading the result, and trying again.

#Agentic Reinforcement Learning#Turn-level Optimization#Tree Search

Reinforcement Learning for Self-Improving Agent with Skill Library

Intermediate

Jiongxiao Wang, Qiaojing Yan et al.Dec 18arXiv

This paper teaches AI agents to learn new reusable skills and get better over time by using reinforcement learning, not just prompts.

#Reinforcement Learning#Skill Library#Sequential Rollout