Papers2

All Beginner Intermediate Advanced

All Sources arXiv

#Importance Sampling

Good SFT Optimizes for SFT, Better SFT Prepares for Reinforcement Learning

Intermediate

Dylan Zhang, Yufeng Xu et al.Feb 1arXiv

The paper shows that a model that looks great after supervised fine-tuning (SFT) can actually do worse after the same reinforcement learning (RL) than a model that looked weaker at SFT time.

#Supervised Fine-Tuning#Reinforcement Learning#Distribution Mismatch

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