Xi Xiao

The work first analyzes latent-token behavior in latent visual reasoning (LVR) and identifies a latent evidence-credit gap: latent tokens respond only weakly to image perturbations that alter the correct answer, which the authors attribute to the lack of explicit supervision during GRPO training; it then proposes ReaLVR, which brings visual-evidence supervision to the model's own free-running latent trajectories, using correct-versus-wrong answer contrast to decide where stronger supervision is needed and relevant-versus-mismatched visual evidence to specify what to preserve, consistently outperforming evaluated LVR baselines across three model families, reaching a 63.7% five-task average on Qwen2.5-VL-7B, and being the first to scale visual reasoning in latent space up to 235B parameters.

The work first analyzes latent-token behavior in latent visual reasoning (LVR) and identifies a latent evidence-credit gap: latent tokens respond only weakly to image perturbations that alter the correct answer, which the authors attribute to the lack of explicit supervision during GRPO training; it then proposes ReaLVR, which brings visual-evidence supervision to the model's own free-running latent trajectories, using correct-versus-wrong answer contrast to decide where stronger supervision is needed and relevant-versus-mismatched visual evidence to specify what to preserve, consistently outperforming evaluated LVR baselines across three model families, reaching a 63.7% five-task average on Qwen2.5-VL-7B, and being the first to scale visual reasoning in latent space up to 235B parameters.