Exploratory Policy Model · Method Overview

A scientific-visualization designer draws a single-page, landscape method overview figure at double-column width, placed at the start of the methods section of a machine-learning paper, explaining an exploratory policy model from theoretical motivation through reward-free pretraining to downstream fine-tuning. Use only generic academic concept symbols; leave identity information and attribution blank. The flow contains three blocks in order — policy space, pretraining and fine-tuning — labeled (a), (b) and (c); the canvas is wide and compact, ready to embed in the text, with titles, axis labels, formulas and annotations clearly readable.

The policy space uses a three-axis 3D plot with a translucent triangular grid for the feasible region, green dots connected by solid lines for policies and a red dot for the goal; leader lines mark the deterministic policy, the maximum state-entropy policy and the theoretical conclusions, and a thick red arrow connects to pretraining. Pretraining and fine-tuning share a blue rounded dashed frame titled “Exploratory Diffusion Model”; pretraining contains a rounded-rectangle experience buffer and trajectory thumbnails. A red robot, a gray robot and a globe stand for the diffusion model, the Gaussian policy and the reward-free environment, linked by solid black arrows, with the environment looping back to the buffer; the connections are labeled interact, collect behavior and write experience. Fine-tuning uses light rounded panels to show the Gaussian policy and the diffusion policy side by side, each with a probability density curve, a small downstream-task box and a red improvement arrow, annotated π_g and π_d. Labels sit close to their icons, arrows and curves, with the legend folded into the figure.

A white background with deep blue as the main color, red and green as sparse accents, light tints separating content; flat shapes, thin tidy lines, a paper-vector style. Keep axis names, direct annotations, formulas and subfigure numbers.

A scientific-visualization designer draws a single-page, landscape method overview figure at double-column width, placed at the start of the methods section of a machine-learning paper, explaining an exploratory policy model from theoretical motivation through reward-free pretraining to downstream fine-tuning. Use only generic academic concept symbols; leave identity information and attribution blank. The flow contains three blocks in order — policy space, pretraining and fine-tuning — labeled (a), (b) and (c); the canvas is wide and compact, ready to embed in the text, with titles, axis labels, formulas and annotations clearly readable.

The policy space uses a three-axis 3D plot with a translucent triangular grid for the feasible region, green dots connected by solid lines for policies and a red dot for the goal; leader lines mark the deterministic policy, the maximum state-entropy policy and the theoretical conclusions, and a thick red arrow connects to pretraining. Pretraining and fine-tuning share a blue rounded dashed frame titled “Exploratory Diffusion Model”; pretraining contains a rounded-rectangle experience buffer and trajectory thumbnails. A red robot, a gray robot and a globe stand for the diffusion model, the Gaussian policy and the reward-free environment, linked by solid black arrows, with the environment looping back to the buffer; the connections are labeled interact, collect behavior and write experience. Fine-tuning uses light rounded panels to show the Gaussian policy and the diffusion policy side by side, each with a probability density curve, a small downstream-task box and a red improvement arrow, annotated π_g and π_d. Labels sit close to their icons, arrows and curves, with the legend folded into the figure.

A white background with deep blue as the main color, red and green as sparse accents, light tints separating content; flat shapes, thin tidy lines, a paper-vector style. Keep axis names, direct annotations, formulas and subfigure numbers.