14 citations · 16 across the 8 of their papers we have counts for
8 papers
Risk-Sensitive RL for Alleviating Exploration Dilemmas in Large Language Models
Yuhua Jiang, Jiawei Huang, Yufeng Yuan +4
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for enhancing Large Language Models (LLMs) on complex reasoning tasks. However, existing methods suffer f…
Truncated Proximal Policy Optimization
Tiantian Fan, Lingjun Liu, Yu Yue +20
Recently, test-time scaling Large Language Models (LLMs) have demonstrated exceptional reasoning capabilities across scientific and professional tasks by generating long chains-of-…
PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier
Yuhua Jiang, Yuwen Xiong, Yufeng Yuan +5
Large Language Models (LLMs) have demonstrated impressive capabilities in complex reasoning tasks, yet they still struggle to reliably verify the correctness of their own outputs.…
VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
Yu Yue, Yufeng Yuan, Qiying Yu +24
We present VAPO, Value-based Augmented Proximal Policy Optimization framework for reasoning models., a novel framework tailored for reasoning models within the value-based paradigm…
A Unified Pairwise Framework for RLHF: Bridging Generative Reward Modeling and Policy Optimization
Wenyuan Xu, Xiaochen Zuo, Chao Xin +3
Reinforcement Learning from Human Feedback (RLHF) has emerged as a important paradigm for aligning large language models (LLMs) with human preferences during post-training. This fr…
Exploring Data Scaling Trends and Effects in Reinforcement Learning from Human Feedback
Wei Shen, Guanlin Liu, Zheng Wu +5
Reinforcement Learning from Human Feedback (RLHF) is crucial for aligning large language models with human preferences. While recent research has focused on algorithmic improvement…
What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret
Yufeng Yuan, Yu Yue, Ruofei Zhu +2
Reinforcement learning (RL) is pivotal for enabling large language models (LLMs) to generate long chains of thought (CoT) for complex tasks like math and reasoning. However, Proxim…
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
Qiying Yu, Zheng Zhang, Ruofei Zhu +32
Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning. However, key technical details…