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20242026
most citedHybridFlow: A Flexible and Efficient RLHF Framework

45 citations · 48 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.LG2026

Towards Full Pipeline FP8 Reinforcement Learning for LLMs

Fanchao Chen, Ziheng Jiang, Ziyun Wei +5

Reinforcement learning (RL) has become a key technique for improving the reasoning and agentic abilities of large language models (LLMs). Although FP8 quantization can accelerate R…

cs.LG2026

GradCuit: Credit-Assigned Gradient Flow Enables Robust and Interpretable Test-Time Latent Reasoning

Zhaoxin Yu, Qi Shen, Hengli Li +4

Optimization-based latent reasoning improves large language model outputs by optimizing instance-specific continuous states at test time while keeping model parameters frozen. Exis…

cs.LG2026

Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization

Huilin Zhou, Jian Zhao, Yilu Zhong +7

Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing approaches often rely on static…

cs.LG2026

Preserve Support, Not Correspondence: Dynamic Routing for Offline Reinforcement Learning

Zhancun Mu, Guangyu Zhao, Yiwu Zhong +1

One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic…

cs.LG2025

Laminar: A Scalable Asynchronous RL Post-Training Framework

Guangming Sheng, Yuxuan Tong, Borui Wan +10

Reinforcement learning (RL) post-training for Large Language Models (LLMs) is now scaling to large clusters and running for extended durations to enhance model reasoning performanc…

cs.LG2025★ 1 cited

FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning

Yuyang Ding, Chi Zhang, Juntao Li +2

Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for enhancing the reasoning capabilities of large language models (LLMs). In this context,…

cs.LG2024★ 45 cited

HybridFlow: A Flexible and Efficient RLHF Framework

Guangming Sheng, Chi Zhang, Zilingfeng Ye +6

Reinforcement Learning from Human Feedback (RLHF) is widely used in Large Language Model (LLM) alignment. Traditional RL can be modeled as a dataflow, where each node represents co…