most citedPixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning

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cs.AI2026

Bad Seeing or Bad Thinking? Rewarding Perception for Multimodal Reasoning

Haozhe Wang, Qixin Xu, Changpeng Wang +4

Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs). Recent advancements have pursued this goal via architectural designs or…

cs.AI2026

RationalRewards: Reasoning Rewards Scale Visual Generation Both Training and Test Time

Haozhe Wang, Cong Wei, Weiming Ren +3

Most reward models for visual generation reduce rich human judgments to a single unexplained score, discarding the reasoning that underlies preference. We show that teaching reward…

cs.AI2025

Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning

Haozhe Wang, Qixin Xu, Che Liu +3

Reinforcement Learning (RL) has proven highly effective at enhancing the complex reasoning abilities of Large Language Models (LLMs), yet underlying mechanisms driving this success…

cs.AI2025

Reverse-Engineered Reasoning for Open-Ended Generation

Haozhe Wang, Haoran Que, Qixin Xu +9

While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical…

cs.AI2025

To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization

Haozhe Wang, Long Li, Chao Qu +4

Recent advances in mathematical problem-solving with language models (LMs) integrate chain-of-thought (CoT) reasoning and code execution to harness their complementary strengths. H…