3 papers
cs.CL2026
Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL
Skylar Zhai, Jingcheng Liang, Dongyeop Kang
Reinforcement fine-tuning improves the reasoning ability of large language models, but it can also encourage them to answer unanswerable queries by guessing or hallucinating missin…
cs.LG2025
Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model
Jing Liang, Hongyao Tang, Yi Ma +5
Reinforcement Learning (RL) has demonstrated its potential to improve the reasoning ability of Large Language Models (LLMs). One major limitation of most existing Reinforcement Fin…
cs.CY2025
LLM Agents for Education: Advances and Applications
Zhendong Chu, Shen Wang, Jian Xie +8
Large Language Model (LLM) agents are transforming education by automating complex pedagogical tasks and enhancing both teaching and learning processes. In this survey, we present…