Oracle Noise optimizes diffusion model noise on a Riemannian hypersphere guided by key prompt words to preserve the Gaussian prior, eliminate norm inflation, and achieve faster semantic alignment than Euclidean methods.
Reinforced attention learning
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VEPO improves RL for visual reasoning by multiplicatively coupling visual sensitivity with token entropy, outperforming entropy-only baselines by 2.28 points at 7B and 3.15 points at 3B scale.
IRA is a stochastic attention mechanism that regulates visual information injection in VLMs to yield smoother embedding trajectories and reduced attention sinks.
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Oracle Noise: Faster Semantic Spherical Alignment for Interpretable Latent Optimization
Oracle Noise optimizes diffusion model noise on a Riemannian hypersphere guided by key prompt words to preserve the Gaussian prior, eliminate norm inflation, and achieve faster semantic alignment than Euclidean methods.
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Entropy Is Not Enough: Unlocking Effective Reinforcement Learning for Visual Reasoning via Vision-Anchored Token Selection
VEPO improves RL for visual reasoning by multiplicatively coupling visual sensitivity with token entropy, outperforming entropy-only baselines by 2.28 points at 7B and 3.15 points at 3B scale.
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Information-Regularized Attention for Visual-Centric Reasoning
IRA is a stochastic attention mechanism that regulates visual information injection in VLMs to yield smoother embedding trajectories and reduced attention sinks.