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Deqing Wang

14 papers hereh-index 684 citations16 works total

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

author position
  • middle author9
  • last author5

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.LG6
  • cs.AI4
  • cs.CL3
  • cs.IR1
same name
  • Deqing Wang — 15 papers, h 14
  • Deqing Wang — 3 papers, h 2
  • Deqing Wang — 2 papers, h 1
  • Deqing Wang — 1 paper

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

most citedContextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards

1 citations · 1 across the 9 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2026

Deferred Exposure of Future Trajectories for Verifiable Reasoning in Autonomous Driving VLMs

Zixuan Huang, Yang Zhou, Kaixuan Wang +7

Recent Vision-Language-Action (VLA) models for autonomous driving (AD) increasingly utilize chain-of-thought (CoT) supervision to enhance the reasoning capabilities of their Vision…

cs.AI2026

Weak-Driven Learning: How Weak Agents make Strong Agents Stronger

Zehao Chen, Gongxun Li, Tianxiang Ai +9

As post-training optimization becomes central to improving large language models, we observe a persistent saturation bottleneck: once models grow highly confident, further training…

cs.AI2026

Does Your Reasoning Model Implicitly Know When to Stop Thinking?

Zixuan Huang, Xin Xia, Yuxi Ren +11

Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thought (CoTs). However, this approa…

cs.AI2026

Real-Time Aligned Reward Model beyond Semantics

Zixuan Huang, Xin Xia, Yuxi Ren +10

Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for aligning large language models (LLMs) with human preferences, yet it is susceptible to reward overoptim…

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