Vision-Language (VL) reasoning requires a model to both extract relevant and accurate information from an image (visual reasoning, VR), and to infer the answer from it (language reasoning, LR). Reinforcement learning with verifiable rewards typically trains both through a single chain-of-thought with a final-answer rew...
Raja Kumar, Rajat Koner, Ritwick Chaudhry et al.· 0 citations
Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management and business operations. Yet this capability remains underexplored: existing benchmarks largely focus on informal, everyday interactions and personal-life scenarios fea...
Ji-Ke Zhong, Ritwick Chaudhry, Xuan-Bai Chen et al.· 0 citations
Diffusion language models (DLMs) enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a persistent quality gap relative to comparably sized autoregressive (AR) models. We attribute this gap to a computation-difficulty mismatch: within a partially observed seq...
Lian-Cheng Fang, Zhuo-Wei Li, Youngeun Kim et al.· 0 citations
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