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cs.AI2026
Credit Where It is Due: Cross-Modality Connectivity Drives Precise Reinforcement Learning for MLLM Reasoning
Zhengbo Jiao, Shaobo Wang, Zifan Zhang +4
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet how visual evidence is…
cs.AI2026
Agentic Proposing: Enhancing Large Language Model Reasoning via Compositional Skill Synthesis
Zhengbo Jiao, Shaobo Wang, Zifan Zhang +5
Advancing complex reasoning in large language models relies on high-quality, verifiable datasets, yet human annotation remains cost-prohibitive and difficult to scale. Current synt…
cs.AI2026
Policy of Thoughts: Scaling Test-Time Training for LLM Reasoning via Online Policy Evolution
Zhengbo Jiao, Hongyu Xian, Qinglong Wang +5
Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption. Current test-time scaling methods treat exec…