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20242026
most citedA Survey of Reinforcement Learning for Large Reasoning Models

2 citations · 8 across the 21 of their papers we have counts for

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7 papers · 1 filter

cs.CL2025

LiveSearchBench: An Automatically Constructed Benchmark for Retrieval and Reasoning over Dynamic Knowledge

Heng Zhou, Ao Yu, Yuchen Fan +10

Evaluating large language models (LLMs) on question answering often relies on static benchmarks that reward memorization and understate the role of retrieval, failing to capture th…

cs.CL2025★ 2 cited

A Survey of Reinforcement Learning for Large Reasoning Models

Kaiyan Zhang, Yuxin Zuo, Bingxiang He +36

In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontie…

cs.RO2025

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning

Haozhan Li, Yuxin Zuo, Jiale Yu +18

Vision-Language-Action (VLA) models have recently emerged as a powerful paradigm for robotic manipulation. Despite substantial progress enabled by large-scale pretraining and super…

cs.CL2025★ 1 cited

SSRL: Self-Search Reinforcement Learning

Yuchen Fan, Kaiyan Zhang, Heng Zhou +15

We investigate the potential of large language models (LLMs) to serve as efficient simulators for agentic search tasks in reinforcement learning (RL), thereby reducing dependence o…

cs.LG2025★ 1 cited

The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

Ganqu Cui, Yuchen Zhang, Jiacheng Chen +14

This paper aims to overcome a major obstacle in scaling RL for reasoning with LLMs, namely the collapse of policy entropy. Such phenomenon is consistently observed across vast RL r…

cs.LG2025

Technologies on Effectiveness and Efficiency: A Survey of State Spaces Models

Xingtai Lv, Youbang Sun, Kaiyan Zhang +8

State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SS…

cs.LG2025★ 2 cited

Process Reinforcement through Implicit Rewards

Ganqu Cui, Lifan Yuan, Zefan Wang +22

Dense process rewards have proven a more effective alternative to the sparse outcome-level rewards in the inference-time scaling of large language models (LLMs), particularly in ta…