3 papers
cs.CL2025
PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models
Ye Yu, Yaoning Yu, Haohan Wang
Large reasoning models (LRMs) such as Claude 3.7 Sonnet and OpenAI o1 achieve strong performance on mathematical benchmarks using lengthy chain-of-thought (CoT) reasoning, but the…
cs.AI2025
LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation
Heng Tan, Hua Yan, Yu Yang
While reinforcement learning (RL) has achieved notable success in various domains, training effective policies for complex tasks remains challenging. Agents often converge to local…
cs.CL2024
WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
Zehan Qi, Xiao Liu, Iat Long Iong +11
Large language models (LLMs) have shown remarkable potential as autonomous agents, particularly in web-based tasks. However, existing LLM web agents heavily rely on expensive propr…