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researcher

Yu Fu

8 papers hereh-index 5223 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author6

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CL6
  • cs.AI1
  • cs.LG1
same name
  • Yu Fu — 21 papers, h 14
  • Yu Fu — 16 papers, h 5
  • Yu Fu — 14 papers, h 11
  • Yu Fu — 7 papers, h 16
  • Yu Fu — 6 papers, h 6
  • Yu Fu — 6 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedA Survey of Reinforcement Learning for Large Reasoning Models

2 citations · 4 across the 8 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CL2025

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

MiroMind Team, Song Bai, Lidong Bing +52

We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale…

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.CL2025

Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions?

Qinyan Zhang, Xinping Lei, Ruijie Miao +18

Large Language Models (LLMs) achieve strong performance on diverse tasks but often exhibit cognitive inertia, struggling to follow instructions that conflict with the standardized…

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.