3 accepted papers
Group Relative Policy Optimization (GRPO), a prominent algorithm within the Reinforcement Learning from Verifiable Rewards (RLVR) framework, has achieved strong results in improving the reasoning capabilities of large language models (LLMs). However, GRPO is prone to advantage collapse, a failure mo…
Large language models (LLMs) have achieved remarkable success in various natural language processing tasks, yet they remain prone to generating factually incorrect outputs—known as "hallucinations". While recent approaches have shown promise for hallucination detection by repeatedly sampling from LL
Tool-augmented Large Language Models (LLMs) provide a robust theoretical foundation for AI agents, with the generation of reasoning plans being a crucial stage. Previous methods for generating reasoning plans primarily rely on In-Context Learning (ICL) or Supervised Fine-Tuning (SFT). However, metho…