Visual Jev Rewards: Reference-Bound Verification for Multi-Subject Image Generation

Key points

• Multi-subject image generation requires rewards that verify whether requested attributes, actions, and relations hold for the specified reference subjects.1
• Each subject-related question receives a positive label only when the requested condition and the relevant reference identities hold jointly.1
• We construct fixed questions offline, train a Qwen3.5-4B verifier with binary supervision, and directly read Yes probabilities from its language-model head.1
• The study provides an initial implementation and evaluation of Visual Jev as a reference-bound reward for multi-subject image generation.1

Sources (1)

• [1]Visual Jev Rewards: Reference-Bound Verification for Multi-Subject Image Generation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:34 AM

  We present reference-bound Visual Jev rewards that turn these visual decisions into generator training signals.

  Multi-subject image generation requires rewards that verify whether requested attributes, actions, and relations hold for the specified reference subjects.

Extractive summary: sentences quoted from the sources.