Exploratory Diffusion Model

Create a method-overview figure by a scientific visualization designer for a machine-learning methods opening. Explain an exploratory policy model’s workflow from theoretical motivation through reward-free pretraining to downstream fine-tuning. Keep content anonymous, limited to general academic concepts and notation, and unattributed.

Use a single-page landscape format proportioned for two-column spanning. Keep it wide and compact for direct paper embedding. Present three continuous sections in sequence, labeled (a), (b), and (c), with legible titles, axis labels, mathematical symbols, and essential annotations.

Section (a): a three-dimensional policy-space diagram with three coordinate axes, a semi-transparent triangular-mesh feasible region, green policy dots joined by solid lines, and one red target dot. Use short leader lines for deterministic and maximum state-entropy policies, plus a brief theoretical conclusion. Connect (a) to (b) with a thick red arrow. Enclose (b) and (c) in a blue rounded dashed border titled “Exploratory Diffusion Model”; title (b) “Pretraining.” Include a rounded replay buffer containing a horizontal row of trajectory thumbnails, paired with red-robot, gray-robot, and globe icons representing the diffusion model, Gaussian policy, and reward-free environment. Connect icons with solid black arrows; loop the environment to the buffer, labeling interaction, behavior collection, and experience storage. Make (c) a light-colored rounded “Fine-tuning” panel showing Gaussian and diffusion policies, each with probability-density curves, a downstream-task box, and a prominent red improvement arrow. Keep π_g and π_d with policy names; attach direct semantic labels to relevant icons, arrows, and curves.

Use a white ground with restrained dark blue, red, and green accents plus light gray and pale beige fills. Keep thin, precise lines in a restrained vector style for an academic paper. Preserve coordinate names, direct labels, formulas, and subfigure labels; limit decoration and attribution to scientific content.

Create a method-overview figure by a scientific visualization designer for a machine-learning methods opening. Explain an exploratory policy model’s workflow from theoretical motivation through reward-free pretraining to downstream fine-tuning. Keep content anonymous, limited to general academic concepts and notation, and unattributed.

Use a single-page landscape format proportioned for two-column spanning. Keep it wide and compact for direct paper embedding. Present three continuous sections in sequence, labeled (a), (b), and (c), with legible titles, axis labels, mathematical symbols, and essential annotations.

Section (a): a three-dimensional policy-space diagram with three coordinate axes, a semi-transparent triangular-mesh feasible region, green policy dots joined by solid lines, and one red target dot. Use short leader lines for deterministic and maximum state-entropy policies, plus a brief theoretical conclusion. Connect (a) to (b) with a thick red arrow. Enclose (b) and (c) in a blue rounded dashed border titled “Exploratory Diffusion Model”; title (b) “Pretraining.” Include a rounded replay buffer containing a horizontal row of trajectory thumbnails, paired with red-robot, gray-robot, and globe icons representing the diffusion model, Gaussian policy, and reward-free environment. Connect icons with solid black arrows; loop the environment to the buffer, labeling interaction, behavior collection, and experience storage. Make (c) a light-colored rounded “Fine-tuning” panel showing Gaussian and diffusion policies, each with probability-density curves, a downstream-task box, and a prominent red improvement arrow. Keep π_g and π_d with policy names; attach direct semantic labels to relevant icons, arrows, and curves.

Use a white ground with restrained dark blue, red, and green accents plus light gray and pale beige fills. Keep thin, precise lines in a restrained vector style for an academic paper. Preserve coordinate names, direct labels, formulas, and subfigure labels; limit decoration and attribution to scientific content.