◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Beidi Chen

11 papers hereh-index 6222 citations14 works total

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

author position
  • middle author4
  • last author7

Across the 11 of 11 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.CL2
  • cs.AI1
  • cs.CR1
  • cs.DC1
same name
  • Beidi Chen — 24 papers, h 19
  • Beidi Chen — 12 papers, h 7
  • Beidi Chen — 11 papers, h 6
  • Beidi Chen — 11 papers, h 10
  • Beidi Chen — 8 papers, h 7
  • Beidi Chen — 3 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 citedMagicPIG: LSH Sampling for Efficient LLM Generation

2 citations · 2 across the 11 of their papers we have counts for

collaborators
Showing 2026 · cs.LGShow all

4 papers · 2 filters

cs.LG2026

Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models

Yang Zhou, Ranajoy Sadhukhan, Zhaofeng Sun +7

Despite being powerful, reinforcement learning with verifiable rewards (RLVR) induces extremely long COT, making it computationally expensive. Since RLVR per-step cost is dominated…

cs.LG2026

The Last Human-Written Paper: Agent-Native Research Artifacts

Jiachen Liu, Jiaxin Pei, Jintao Huang +34

Scientific publication compresses a branching, iterative research process into a linear narrative, discarding the majority of what was discovered along the way. This compilation im…

cs.LG2026

AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs

Haizhong Zheng, Yizhuo Di, Jiahui Wang +7

Reinforcement learning (RL) is increasingly used to improve the reasoning, coding, and tool-use capabilities of large language models, but agentic RL remains prohibitively expensiv…

cs.LG2026

STEM: Scaling Transformers with Embedding Modules

Ranajoy Sadhukhan, Sheng Cao, Harry Dong +5

Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication ove…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.