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Where-OPD

Official implementation of: Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes

This is 1 of 150 launches in graphics, 3D, and animation tools — see how it stacks up on momentum and crowding →

1023 other launches read as similar to this one →

Details

External ID
1398381847
Source
GITHUB
Company
—
Product
Where-OPD
Website domain
github.com
Launched
Sept. 30, 2026
Cohort
—
Upvotes
10
Upvotes percentile
0.2744874715261959
Tags
—
Fetched at
Oct. 4, 2026, 5:02 p.m.
Updated at
Oct. 4, 2026, 5:02 p.m.

Enrichment

Theme
graphics, 3D, and animation tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
self-distillation framework for multimodal llms
Manually corrected
False

Could you build this?

No This is a novel AI research implementation involving on-policy self-distillation and spatial guidance for multimodal LLMs using synthetic scene generation. Creating this requires original machine learning research, specialized 3D/spatial synthetic data pipelines, and heavy GPU cluster compute.

What it would actually take: Implementation requires a distributed PyTorch / DeepSpeed or Megatron training pipeline integrating multimodal architectures (like LLaVA or Qwen-VL). The core challenges are designing the spatial reasoning formulation, building a high-fidelity 3D synthetic data generation engine, and running compute-intensive on-policy reinforcement/distillation algorithms across clusters of H100/A100 GPUs.

Competitors

Other products that read as similar to this one — 1023 launches clear the similarity bar, closest 8 shown.

Attention rank: #687 of 1024 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 334 days after the earliest competitor.

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