6 papers
Distribution-Centric Policy Optimization Dominates Exploration-Exploitation Trade-off
Zhaochun Li, Chen Wang, Jionghao Bai +4
The exploration-exploitation (EE) trade-off is a central challenge in reinforcement learning (RL) for large language models (LLMs). With Group Relative Policy Optimization (GRPO),…
CAMF: Collaborative Adversarial Multi-agent Framework for Machine Generated Text Detection
Yue Wang, Liesheng Wei, Yuxiang Wang
Detecting machine-generated text (MGT) from contemporary Large Language Models (LLMs) is increasingly crucial amid risks like disinformation and threats to academic integrity. Exis…
No Free Lunch: Rethinking Internal Feedback for LLM Reasoning
Yanzhi Zhang, Zhaoxi Zhang, Haoxiang Guan +6
Reinforcement learning has emerged as a powerful paradigm for post-training large language models (LLMs) to improve reasoning. Approaches like Reinforcement Learning from Human Fee…
EFRame: Deeper Reasoning via Exploration-Filter-Replay Reinforcement Learning Framework
Chen Wang, Lai Wei, Yanzhi Zhang +5
Recent advances in reinforcement learning (RL) have significantly enhanced the reasoning capabilities of large language models (LLMs). Group Relative Policy Optimization (GRPO), a…
First SFT, Second RL, Third UPT: Continual Improving Multi-Modal LLM Reasoning via Unsupervised Post-Training
Lai Wei, Yuting Li, Chen Wang +4
Improving Multi-modal Large Language Models (MLLMs) in the post-training stage typically relies on supervised fine-tuning (SFT) or reinforcement learning (RL), which require expens…
Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start
Lai Wei, Yuting Li, Kaipeng Zheng +5
Recent advancements in large language models (LLMs) have demonstrated impressive chain-of-thought reasoning capabilities, with reinforcement learning (RL) playing a crucial role in…